Strategies of transfer learning according to the size of the new dataset and its similarity to the one used in pre-trained model.
\r\n\tIt is a relatively simple process and a standard tool in any industry. Because of the versatility of the titration techniques, nearly all aspects of society depend on various forms of titration to analyze key chemical compounds.
\r\n\tThe aims of this book is to provide the reader with an up-to-date coverage of experimental and theoretical aspects related to titration techniques used in environmental, pharmaceutical, biomedical and food sciences.
Human target analysis is acknowledged to be useful for a wide range of security and safety applications, such as through-wall detection and ground surveillance [1, 2]. The analysis has usually been conducted in the time-frequency (micro-Doppler) and time-range (high-range resolution profile) domains. Chen first introduced the micro-Doppler concept to the radar community in [3]. Time-frequency transforms, such as the short-time Fourier transform, are used to analyze target Doppler signatures in slow time. Since then, studies have investigated micro-Doppler-based target feature analysis [4, 5, 6, 7, 8, 9]. The high-range resolution profile (HRRP) of human targets has also been studied [10]. Micro-Doppler profile and HRRP, which are both generated by micromotions, have their shortcomings: they only contain information from either the time-frequency or time-range domain. Micro-Doppler analysis neglects range information, while HRRP analysis neglects Doppler information.
\nTherefore, in order to analyze the target signature more comprehensively, we describe a new concept called range-Doppler surface (RDS). As an alternative to the micro-Doppler profile and HRRP, RDS is a radar target representation extracted from a three-dimensional data cube—the range-Doppler (RD) video sequence [11, 12]. The RDS consists of all the important information contained in both HRRP and micro-Doppler signatures. The present study analyzes the RDS using simulated and real radar data. Results suggest a new area of human target analysis and classification.
\nIt is worth mentioning that the term “range-Doppler surface” has been presented in prior works [13, 14, 15]. It was used for a 2D range-Doppler image that is shown in a 3D perspective. In this chapter we present this term to describe the time-varying range-Doppler isosurface information. RDS is referred to 3D visualization for the first time in this study, and it is indeed a suitable, precise term to describe this concept.
\nNowadays, deep learning has become a mainstream method for human activity recognition instead of conventional machine learning methods. Deep learning came into our sight and has emerged as a hot topic in the past few years. It works by learning several layers of representation for modeling the complex relationships among data. It can create high-level features from related low-level ones by means of its hierarchical architecture without artificial extraction from the raw data and specialized knowledge. In this way, it makes activity recognition system more intelligent and versatile. Therefore, deep learning is an applicable approach to identify human activities.
\nThis chapter is organized as follows: In Section 2, human target analysis with the micro-Doppler profile, HRRP, as well as the three-dimensional RD video sequence is described. In Section 3, the range-Doppler surface is described. Then, deep learning for human activity classification is introduced briefly in Section 4. Finally, future directions are drawn in Section 5.
\nThe RD video sequence that consists of N time sampled 2D range-Doppler images contains both spatial and temporal characteristics: range and frequency information consists in every RD image, while time information exists among sequential frames. Compared with 1D and 2D forms of radar echoes, the joint time-/range-frequency form of echoes contains all the targets’ motion information.
\nAmong these human target analysis systems using the RD video sequence, so far, a representative example is Google Project Soli, which is the first gesture recognition system capable of recognizing a rich set of dynamic gestures based on short-range radar [27, 28]. It is based on an end-to-end trained combination of deep convolutional and recurrent neural networks, and the dataset is comprised of 3D data cubes. Combining CNN and RNN could enhance the ability to recognize different activities that vary in temporal and spatial dimensions. The system can recognize subtle gestures of 10 kinds performed by 10 people. From then on, many researches have been done using radar just like Kinect and Leap Motion in CV [21]. In addition, with the advent of this system, many novel ideas have been proposed based on it [22, 29, 30].
\nAlthough containing abundant information of human activity properties, 3D form of human backscattering echoes is complicated to process. As a result, the complexity of the systems using 3D form of echoes is higher compared with those using 2D forms. 2D forms of radar echoes, which mainly refer to HRRP and micro-Doppler profiles, also carry enough human activity information and can be used for human target analysis.
\nIt is a common way to obtain time-Doppler maps, namely, joint time-frequency transformation (JTFA) [23, 24]. Similar to the developments in other fields such as acoustics and speech processing, JTFA can provide additional insight into the analysis, interpretation, and processing of radar signals, and the performance is superior to what has been achieved in the traditional time or frequency domain alone [3].
\nThe short-time Fourier transform (STFT) is the most commonly used time-frequency transform. STFT performs the Fourier transform on a short-time sliding window basis instead of using one long-time window to the entire signal. The square modulus of the STFT is called the spectrogram, which is a nonnegative time-frequency energy distribution without phase information [20]:
\nThe resolution of STFT is determined by the size of window function. There is a trade-off between the time resolution and the frequency resolution [4].
\nBy performing a STFT over time for every range bin, a series of 2D time-Doppler images along range can be acquired. Then, summing the time-Doppler “video” along range, a time-Doppler map is obtained.
\nAmong these three 2D forms of echoes, till now, the time-Doppler maps are most commonly used to analyze human targets. The time-Doppler maps include a wealth of Doppler information changing over time. The main Doppler shift is caused by the bulk speed, while micro-Doppler is produced by rotating or vibrating parts, such as the legs, feet, and hand. By selecting and classifying the micro-Doppler features in the time-Doppler map, human activities can be recognized by various models. For example, G. Klarenbeek et al. applied a LSTM structure with the time-Doppler maps to realize the multi-target human gait classification [26].
\nTime-range maps contain time-varying range information between the target and the radar. When a person is moving, different components of the human body have different relative distances from the radar at time t. Therefore, various time-range maps produced by different activities can be used to recognize the corresponding activities, although they neglect the Doppler information. In [25], Yuming Shao et al. have employed the time-range maps with a deep CNN to classify human motions, and a good performance was achieved.
\nIn general, separating the frequency components of different body parts is a vital step for human target analysis. However, the ability to resolve separate frequency components is limited because of the time-varying Doppler shifts in radar echoes. A general way of representing the scattered signals is range-Doppler (RD) processing. And a classical way for RD processing is to apply Fourier transform to samples from a fixed range bin over one coherent processing interval. This interval is theoretically limited by the time during which the target stays in the same range bin:
\nThe radar transmits a coherent burst of M pulses:
\nwhere \n
where \n
where \n
where all the constant terms have been absorbed and the variable name has been kept as \n
In radar, we usually assume that the target remains static in one pulse duration \n
where \n
FT is performed over \n
where \n
However, a target may traverse multiple range cells in one coherent processing interval sometimes. In this situation, applying conventional Fourier transform-based RD processing is not suitable, which will lead to a blur in frequency. To improve the Doppler resolution, a Keystone transform-based range-Doppler processing is proposed in this chapter.
\nBy performing RD processing on the radar echoes, a sequence of RD images is obtained. The three-dimensional RD video is proposed to describe the slow-time evolution of target RD signatures.
\nBefore constructing the RDS, it is essential to detect the target in the range-Doppler domain, since detection allows the extraction of targets and elimination of false alarms. The cell-average constant false alarm rate (CA-CFAR) procedure [16] is a classical approach of detecting a target in noise and clutter. Detection is performed employing a two-dimensional CA-CFAR procedure [17] in the range-Doppler domain. For each range-Doppler image in the range-Doppler video sequence, a sliding 2D window is applied to scan this RD image pixel by pixel. For each pixel, it is claimed as detected if its intensity exceeds an estimated threshold. In Figure 1, a typical 2D window is shown. The cell under test covers the target reflections. The reference cells estimate background noise for computing the detection threshold. The guard cells separate the cell under test and reference cells as a barrier. The sizes of these cells strongly affect the performance of the CA-CFAR detection and thus should be tuned carefully according to radar parameters and target characteristics (e.g., signal bandwidth, maximum unambiguous Doppler, and target velocity).
\n2D CA-CFAR window [27] (red: cell under test; yellow: guard cells; green: reference cells).
In Figure 2a, the detected scatterers of a simulated human target are shown in a three-dimensional (3D) volume, where the intensities of different scatterers are represented by various colors. Note that the simulated radar system uses the same parameters as used in generating Figure 3. Finally, the RDS (see Figure 2b) is constructed by creating a surface that has the same intensity value within the 3D range-Doppler-time volume (i.e., range-Doppler video sequence) in Figure 2a. Isosurface plots are similar to contour plots in that they both indicate where values are equal. The MATLAB® function isosurface is applied to extract the isosurface from the volume using a user-defined isosurface threshold. The isosurface connects points that have the specified value much the way contour lines connect points of equal elevation. Note that the difference of the surface color in Figure 2b is not due to different intensities, but due to the lighting effect used to illustrate the 3D object in MATLAB®. Selecting a reasonable threshold is important in this procedure, because this affects the final output significantly. Although currently the threshold is set manually, automatic approaches to construct the volume surface are certainly interesting in future studies.
\nRange-Doppler surface of a simulated walking human target.
Range-velocity images of one simulated human gait.
Target analysis has been commonly done in the time-range domain or time-frequency domain. As mentioned above, HRRP neglects Doppler information, while micro-Doppler neglects range information. Furthermore, micro-Doppler is difficult to be used in multi-target situations, since the Doppler spectrums of different targets may overlap. The RDS shows the target surface in the 3D range-Doppler-time space. All the important targets’ information, which might be contained in HRRP and micro-Doppler, is included in RDS.
\nThe RDS of different body segments is presented in Figure 4. The responses of the feet are well separated in either range or Doppler, and the responses of the thorax and hands overlap with each other. The feet have a larger Doppler offset than the thorax and hands.
\nRange-Doppler surface of different body segments (a) RDS of feet, (b) Time-range projection of RDS in (a), (c) Time-velocity projection of RDS in (a), (d) RDS of thorax + hands, (e) Time-range projection of RDS in (d), (f) Time-velocity projection of RDS in (d).
PulsOn 400 radar system, manufactured by Time Domain Corporation [18], was used to acquire measurement data (experimental setup; see Figure 5). Its operational frequency band is 3.1–5.3 GHz, and the signal is modulated by an m-sequence. The transmitted power is −14.5 dBm. The pulse repetition frequency is 200 Hz, and the sampling frequency is 16.39 GHz. More details about this radar are given in the literature [19].
\nRadar system and experimental setup.
In the measurement, two scenarios were considered: single-person walking and two-people walking. The RDSs generated for these two scenarios are presented in Figures 6 and 7, respectively. The RDS for the single-person scenario is similar to the simulated RDS shown in Figure 2b, and the capability of RDS to separate body segments is demonstrated again in Figure 6d and e. It should be noted that in the processing for real data, static clutter is removed via moving target indication before constructing RDS, which cancels the clutter and also the stationary parts of the human body. More precisely, in each walking step, the reflection from the stationary leg/foot is rejected, while the reflection of the moving leg/foot is retained.
\nRange-Doppler surface of two human targets (threshold = −20 dB).
Range-Doppler surface of two human targets (threshold = −23 dB).
In Figure 7, the RDS of the two-people scenario shows that the backscattering of the human targets is automatically separated in the 3D range-Doppler-time space. This indicates that RDS is not only able to show the range-Doppler signatures of a single extended target but also able to separate (or even track) multiple targets in the range-Doppler video sequence. Additional processing to separate multi-target reflection (e.g., the separating method proposed in [19]) is not required anymore.
\nAs an example, the RDS has been demonstrated for human target analysis using an S-/C-band UWB radar, but RDS itself is in fact a generic tool. It can be used in various applications, such as feature extraction, tracking, or classification. Similar to micro-Doppler or high-resolution range profiles, the RDS has the potential of being used to analyze different types of targets (e.g., people or animals) and also used in different types of radars (e.g., S-band, C-band, or X-band radar).
\nWith the rapid emergence of new deep learning algorithms and architectures, the development of many domains such as speech recognition, visual object recognition, object detection, and even drug discovery and genomics has been accelerated. Deep learning is composed of multiple processing layers to learn high-level representations with multiple levels of abstraction, thus automating the process of feature extraction. Hence, deep models do not need heavy feature engineering and domain knowledge compared with traditional machine learning techniques. What is more, with so many complicated deep-level transformations, very complex functions can be learned, and more classification and recognition problems can be solved. As a result, deep learning has made great contributions in overcoming difficulties in artificial intelligence and advancing the development of artificial intelligence.
\nNext, we will mainly describe several deep learning models, which are used mostly in human target analysis field.
\nConvolutional neural network (CNN) is inspired by the visual cortex structure which is composed of simple cells and complex cells. It adopts four key ideas: local connections, parameter sharing, pooling, and multilayers. In this way, CNN is able to fully explore the property of raw signals that there are compositional hierarchies, namely, extracting higher-level features from the lower-level ones. As a result, convolutional neural networks, as one of the representative algorithms of deep learning, have made a remarkable progress in object detection and recognition, natural language processing (NLP), speech recognition, and medical image analysis in the past few years. In human activity recognition field, CNN is one of the most used deep learning models. For instance, Zhenyuan Zhang et al. have adopted this network to realize continuous dynamic gesture recognition using a radar sensor [30], while Youngwook Kim et al. detected and classified human activities using deep convolutional neural networks [32].
\nWith the successful application in NLP, recurrent neural network (RNN) has caught researchers’ attention. RNNs have shone light on modeling temporal sequences such as texts and speeches because they can mine timing and semantic information in them. From the perspective of network structure, RNN can remember the previous information and use it to influence the output of the following nodes. However, conventional RNN has its own limit: long-term dependencies. To overcome this problem, long short-term memory (LSTM) came into being and performed better in many tasks. LSTM owns three special gates: input gate, output gate, and forget gate. By using these memory units especially the forget gate, LSTM can access a long-range context of the sequential data. Due to these advantages above, many human activity recognition systems adopted RNN and its variants. Zhi Zhou et al. adopted multimodal signals, including HRRPs and Doppler profiles, which are acquired by the terahertz radar system to recognize dynamic gestures, and the recognition rate reaches more than 91% [22].
\nAuto-encoder is a high-performance deep learning network suitable for dealing with one-dimensional data by extracting optimized deep features. It learns a deep feature representation of raw input via several rounds of encoding-decoding procedures. Auto-encoder applies the layer-wise greedy unsupervised pre-training principle so as to quickly obtain an efficient deep network.
\nThe commonly used variants of auto-encoder are mainly the following kinds: (1) sparse auto-encoder, which is able to rebuild the input data well, and (2) de-noising auto-encoder and contractive auto-encoder which can make the models more generic by adding noise or a well-chosen penalty term.
\nAuto-encoder is able to provide a powerful feature extraction approach for many tasks, which saves a lot of labor. In this way, auto-encoder can combine with whether conventional machine learning algorithm or other deep learning models and becomes a more robust one. Mehmet Saygin Seyfiolu et al. [33] used a convolutional auto-encoder architecture to discriminate 12 indoor activity classes involving aided and unaided human motions by recognizing different 2D Doppler maps, and Branka Jokanovic et al. [34] applied three stacked auto-encoders to extract deep features, respectively, and fuse the result together with a voting principle to classify activities.
\nDespite plentiful human target analysis researches have been done with all kinds of deep learning methods and the effect is considerable, there are still many challenges and opportunities. Next, a few future research considerations will be listed below.
\nAmong three forms of backscattered radar signals mentioned above, 2D domain radar signals such as time-Doppler maps and time-range maps are mostly used for recognition because they are represented in two dimensions and look more intuitive. Furthermore, these deep learning models are usually introduced from the field of computer vision. In CV area, the images are natural images but the radar images are not. This will lead a doubt that it is proper or not to treat radar 2D images as natural images completely. As a result, it is very urgent to create some techniques to distinguish more radar images with natural images.
\nCommon energy-based power spectrograms after FT or STFT always abandon the phase information in backscatter echoes. But phase is an important attribute of any signal and contains a wealth of information such as transmission duration and distance. Pavlo Molchanov et al. investigated frequency and phase coupling phenomena for radar backscattered signals and proposed novel bicoherence-based information features [31]. We think phrase information in radar backscattering signals should be considered more in future studies.
\nDoppler shift is caused by the radial velocity of the moving target. The radial velocity changes with the position of the target and the radar because it is the component of the object’s velocity. In other words, when the radar is above the pedestrian, the Doppler is partly induced by the motion vertical component such as arm and leg vertical motions. In this case, negative Doppler will appear. As a result, if the relative position is different, radar backscattered signals produced by one subject performing a specified activity will differ a lot. How to overcome the orientation sensitivity of radar-based HAR is one of the future research topics.
\nThrough the investigation of the current research status, compared with the researches in 2D domain, there are few research results on 1D and 3D domains of human echo signals, but through the discussion in previous chapters, we have reason to believe that the two forms of echoes have enough development potential and explore space. Thus, more attention should be paid to this part of human target analysis field.
\nI would like to express my gratitude to Pavlo Molchanov, Takuya Sakamoto, Pascal Aubry, Francois Le Chevalier, and Alexander Yarovoy for their contribution. I thank them for assisting and supporting and for their advisable opinions.
\nEarly approaches of artificial intelligence (AI) have sought solutions through formal representation of knowledge and applying logical inference rules. Later on, with having more data available, machine learning approaches prevailed which have the capability of learning from data. Many successful examples today, such as language translation, are results of this data-driven approach. When compared to other machine learning approaches, deep learning (deep artificial neural networks) has two advantages. It benefits well from vast amount of data—more and more of what we do is recorded every day, and it does not require defining the features to be learned beforehand. As a consequence, in the last decade, we have seen numerous success stories achieved with deep learning approaches especially with textual and visual data.
In this chapter, first a relatively short history of neural networks will be provided, and their main principles will be explained. Then, the chapter will proceed to two parallel paths. The first path treats text data and explains the use of deep learning in the area of natural language processing (NLP). Neural network methods first transformed the core task of language modeling. Neural language models have been introduced, and they superseded n-gram language models. Thus, initially the task of language modeling will be covered. The primary focus of this part will be representation learning, where the main impact of deep learning approaches has been observed. Good dense representations are learned for words, senses, sentences, paragraphs, and documents. These embeddings are proved useful in capturing both syntactic and semantic features. Recent works are able to compute contextual embeddings, which can provide different representations for the same word in different contextual units. Consequently, state-of-the-art embedding methods along with their applications in different NLP tasks will be stated as the use of these pre-trained embeddings in various downstream NLP tasks introduced a substantial performance improvement.
The second path concentrates on visual data. It will introduce the use of deep learning for computer vision research area. In this aim, it will first cover the principles of convolutional neural networks (CNNs)—the fundamental structure while working on images and videos. On a typical CNN architecture, it will explain the main components such as convolutional, pooling, and classification layers. Then, it will go over one of the main tasks of computer vision, namely, image classification. Using several examples of image classification, it will explain several concepts related to training CNNs (regularization, dropout and data augmentation). Lastly, it will provide a discussion on visualizing and understanding the features learned by a CNN. Based on this discussion, it will go through the principles of how and when transfer learning should be applied with a concrete example of real-world four-class classification problem.
Deep neural networks currently provide the best solutions to many problems in computer vision and natural language processing. Although we have been hearing the success news in recent years, artificial neural networks are not a new research area. In 1943, McCulloch and Pitts [1] built a neuron model that sums binary inputs, and outputs
A neuron that mimics the behavior of logical AND operator. It multiplies each input (x1 and x2) and the bias unit +1 with a weight and thresholds the sum of these to output 1 if the sum is big enough (similar to our neurons that either fire or not).
In 1957, Rosenblatt introduced perceptrons [2]. The idea was not different from the neuron of McCulloch and Pitts, but Rosenblatt came up with a way to make such artificial neurons learn. Given a training set of input-output pairs, weights are increased/decreased depending on the comparison between the perceptron’s output and the correct output. Rosenblatt also implemented the idea of the perceptron in custom hardware and showed it could learn to classify simple shapes correctly with 20 × 20 pixel-like inputs (Figure 2).
Mark I Perceptron at the Cornell Aeronautical Laboratory, hardware implementation of the first perceptron (source: Cornell University Library [3]).
Marvin Minsky who was the founder of MIT AI Lab and Seymour Papert together wrote a book related to the analysis on the limitations of perceptrons [4]. In this book, as an approach of AI, perceptrons were thought to have a dead end. A single layer of neurons was not enough to solve complicated problems, and Rosenblatt’s learning algorithm did not work for multiple layers. This conclusion caused a declining period for the funding and publications on AI, which is usually referred to as “AI winter.”
Paul Werbos proposed that backpropagation can be used in neural networks [5]. He showed how to train multilayer perceptrons in his PhD thesis (1974), but due to the AI winter, it required a decade for researchers to work in this area. In 1986, this approach became popular with “Learning representations by back-propagating errors” by Rumelhart et al. [6]. First time in 1989, it was applied to a computer vision task which is handwritten digit classification [7]. It has demonstrated excellent performance on this task. However, after a short while, researchers started to face problems with the backpropagation algorithm. Deep (multilayer) neural networks trained with backpropagation did not work very well and particularly did not work as well as networks with fewer layers. It turned out that the magnitudes of backpropagated errors shrink very rapidly and this prevents earlier layers to learn, which is today called as “the vanishing gradient problem.” Again it took more than a decade for computers to handle more complex tasks. Some people prefer to name this period as the second AI winter.
Later, it was discovered that the initialization of weights has a critical importance for training, and with a better choice of nonlinear activation function, we can avoid the vanishing gradient problem. In the meantime, our computers got faster (especially thanks to GPUs), and huge amount of data became available for many tasks. G. Hinton and two of his graduate students demonstrated the effectiveness of deep networks at a challenging AI task: speech recognition. They managed to improve on a decade-old performance record on a standard speech recognition dataset. In 2012, a CNN (again G. Hinton and students) won against other machine learning approaches at the Large Scale Visual Recognition Challenge (ILSVRC) image classification task for the first time.
Technically any neural network with two or more hidden layers is “deep.” However, in papers of recent years, deep networks correspond to the ones with many more layers. We show a simple network in Figure 3, where the first layer is the input layer, the last layer is the output layer, and the ones in between are the hidden layers.
A simple neural network with two hidden layers. Entities plotted with thicker lines are the ones included in Eq. (1), which will be used to explain the vanishing gradient problem.
In Figure 3,
Activation function is the element that gives a neural network its nonlinear representation capacity. Therefore, we always choose a nonlinear function. If activation function was chosen to be a linear function, each layer would perform a linear mapping of the input to the output. Thus, no matter how many layers were there, since linear functions are closed under composition, this would be equivalent to having a single (linear) layer.
The choice of activation function is critically important. In early days of multilayer networks, people used to employ
Eq. (1) shows how the error in the final layer is backpropagated to a neuron in the first hidden layer, where
Figure 4 shows the derivative of
Derivative of the sigmoid function.
Today, choices of activation function are different. A rectified linear unit (ReLU), which outputs zero for negative inputs and identical value for positive inputs, is enough to eliminate the vanishing gradient problem. To gain some other advantages, leaky ReLU and parametric ReLU (negative side is multiplied by a coefficient) are among the popular choices (Figure 5).
Plots for some activation functions. Sigmoid is on the left, rectified linear unit is in the middle, and leaky rectified linear unit is on the right.
Deep learning transformed the field of natural language processing (NLP). This transformation can be described by better representation learning through newly proposed neural language models and novel neural network architectures that are fine-tuned with respect to an NLP task.
Deep learning paved the way for neural language models, and these models introduced a substantial performance improvement over n-gram language models. More importantly, neural language models are able to learn good representations in their hidden layers. These representations are shown to capture both semantic and syntactic regularities that are useful for various downstream tasks.
Representation learning through neural networks is based on the distributional hypothesis: “words with similar distributions have similar meanings” [9] where distribution means the neighborhood of a word, which is specified as a fixed-size surrounding window. Thus, the neighborhoods of words are fed into the neural network to learn representations implicitly.
Learned representations in hidden layers are termed as distributed representations [10]. Distributed representations are local in the sense that the set of activations to represent a concept is due to a subset of dimensions. For instance, cat and dog are hairy and animate. The set of activations to represent “being hairy” belongs to a specific subset of dimensions. In a similar way, a different subset of dimensions is responsible for the feature of “being animate.” In the embeddings of both cat and dog, the local pattern of activations for “being hairy” and “being animate” is observed. In other words, the pattern of activations is local, and the conceptualization is global (e.g., cat and dog).
The idea of distributed representation was realized by [11] and other studies relied on it. Bengio et al. [11] proposed a neural language model that is based on a feed-forward neural network with a single hidden layer and optional direct connections between input and output layers.
The first breakthrough in representation learning was word2vec [12]. The authors removed the nonlinearity in the hidden layer in the proposed model architecture of [11]. This model update brought about a substantial improvement in computational complexity allowing the training using billions of words. Word2vec has two variants: continuous bag-of-words (CBOW) and Skip-gram.
In CBOW, a middle word is predicted given its context, the set of neighboring left and right words. When the input sentence “creativity is intelligence having fun” is processed, the system predicts the middle word “intelligence” given the left and right contexts (Figure 6). Every input word is in one-hot encoding where there is a vocabulary size (
CBOW architecture.
In Skip-gram, the system predicts the most probable context words for a given input word. In terms of a language model, while CBOW predicts an individual word’s probability, Skip-gram outputs the probabilities of a set of words, defined by a given context size. Due to high dimensionality in the output layer (all vocabulary words have to be considered), Skip-gram has higher computational complexity than CBOW (Figure 7). To deal with this issue, rather than traversing all vocabulary in the output layer, Skip-gram with negative sampling (SGNS) [13] formulates the problem as a binary classification where one class represents the current context’s occurrence probability, whereas the other is all vocabulary terms’ occurrence in the present context. In the latter probability calculation, a sampling approach is incorporated. As vocabulary terms are not distributed uniformly in contexts, sampling is performed from a distribution where the order of the frequency of vocabulary words in corpora is taken into consideration. SGNS incorporates this sampling idea by replacing the Skip-gram’s objective function. The new objective function (Eq. (3)) depends on maximizing
Skip-gram architecture.
Both word2vec variants produced word embeddings that can capture multiple degrees of similarity including both syntactic and semantic regularities.
A regular extension to word2vec model was doc2vec [14], where the main goal is to create a representation for different document levels, e.g., sentence and paragraph. Their architecture is quite similar to the word2vec except for the extension with a document vector. They generate a vector for each document and word. The system takes the document vector and its words’ vectors as an input. Thus, the document vectors are adjusted with regard to all the words in this document. At the end, the system provides both document and word vectors. They propose two architectures that are known as distributed memory model of paragraph vectors (DM) and distributed bag-of-words model of paragraph vectors (DBOW).
DM: In this architecture, inputs are the words in a context except for the last word and document, and the output is the last word of the context. The word vectors and document vector are concatenated while they are fed into the system.
DBOW: The input of the architecture is a document vector. The model predicts the words randomly sampled from the document.
An important extension to word2vec and its variants is fastText [15], where they considered to use characters together with words to learn better representations for words. In fastText language model, the score between a context word and the middle word is computed based on all character n-grams of the word as well as the word itself. Here n-grams are contiguous sequences of
The idea of using the smallest syntactic units in the representation of words introduced an improvement in morphologically rich languages and is capable to compute a representation for out-of-vocabulary words.
The recent development in representation learning is the introduction of contextual representations. Early word embeddings have some problems. Although they can learn syntactic and semantic regularities, they are not so good in capturing a mixture of them. For example, they can capture the syntactic pattern look-looks-looked. In a similar way, the words hard, difficult, and tough are embedded into closer points in the space. To address both syntactic and semantic features, Kim et al. [16] used a mixture of character- and word-level features. In their model, at the lowest level of hierarchy, character-level features are processed by a CNN; after transferring these features over a highway network, high-level features are learned by the use of a long short-term memory (LSTM). Thus, the resulting embeddings showed good syntactic and semantic patterns. For instance, the closest words to the word richard are returned as eduard, gerard, edward, and carl, where all of them are person names and have syntactic similarity to the query word. Due to character-aware processing, their models are able to produce good representations for out-of-vocabulary words.
The idea of capturing syntactic features at a low level of hierarchy and the semantic ones at higher levels was realized ultimately by the Embeddings from Language Models (ELMo) [17]. ELMo proposes a deep bidirectional language model to learn complex features. Once these features are learned, the pre-trained model is used as an external knowledge source to the fine-tuned model that is trained using task-specific data. Thus, in addition to static embeddings from the pre-trained model, contextual embeddings can be taken from the fine-tuned one.
Another drawback of previous word embeddings is they unite all the senses of a word into one representation. Thus, different contextual meanings cannot be addressed. The brand new ELMo and Bidirectional Encoder Representations from Transformers (BERT) [18] models resolve this issue by providing different representations for every occurrence of a word. BERT uses bidirectional Transformer language model integrated with a masked language model to provide a fine-tuned language model that is able to provide different representations with respect to different contexts.
In NLP, different neural network solutions have been used in various downstream tasks.
Language data are temporal in nature so recurrent neural networks (RNNs) seem as a good fit to the task in general. RNNs have been used to learn long-range dependencies. However, because of the dependency to the previous time steps in computations, they have efficiency problems. Furthermore, when the length of sequences gets longer, an information loss occurs due to the vanishing gradient problem.
Long short-term memory architectures are proposed to tackle the problem of information loss in the case of long sequences. Gated recurrent units (GRUs) are another alternative to LSTMs. They use a gate mechanism to learn how much of the past information to preserve at the next time step and how much to erase.
Convolutional neural networks have been used to capture short-ranging dependencies like learning word representation over characters and sentence representation over its n-grams. Compared to RNNs, they are quite efficient due to independent processing of features. Moreover, through the use of different convolution filter sizes (overlapping localities) and then concatenation, their learning regions can be extended.
Machine translation is a core NLP task that has witnessed innovative neural network solutions that gained wide application afterwards. Neural machine translation aims to translate sequences from a source language into a target language using neural network architectures. Theoretically, it is a conditional language model where the next word is dependent on the previous set of words in the target sequence and the source sentence at the same time. In traditional language modeling, the next word’s probability is computed based solely on the previous set of words. Thus, in conditional language modeling, conditional means conditioned on the source sequence’s representation. In machine translation, source sequence’s processing is termed as encoder part of the model, whereas the next word prediction task in the target language is called decoder. In probabilistic terms, machine translation aims to maximize the probability of the target sequence
This conditional probability calculation can be conducted by the product of component conditional probabilities at each time step where there is an assumption that the probabilities at each time step are independent from each other (Eq. (6)).
The first breakthrough neural machine translation model was an LSTM-based encoder-decoder solution [19]. In this model, source sentence is represented by the last hidden layer of encoder LSTM. In the decoder part, the next word prediction is based on both the encoder’s source representation and the previous set of words in the target sequence. The model introduced a significant performance boost at the time of its release.
In neural machine translation, the problem of maximizing the probability of a target sequence given the source sequence can be broken down into two components by applying Bayes rule on Eq. (5): the probability of a source sequence given the target and the target sequence’s probability (Eq. (7)).
In this alternative formulation,
Bandanau et al. [20] propose an attention mechanism to directly connect to each word in the encoder part in predicting the next word in each decoder step. This mechanism provides a solution to alignment in that every word in translation is predicted by considering all words in the source sentence, and the predicted word’s correspondences are learned by the weights in the attention layer (Figure 8).
Sequence-to-sequence attention.
Attention is a weighted sum of values with respect to a query. The learned weights serve as the degree of query’s interaction with the values at hand. In the case of translation, values are encoder hidden states, and query is decoder hidden state at the current time step. Thus, weights are expected to show each translation step’s grounding on the encoder hidden states.
Eq. (8) gives the formulae for an attention mechanism. Here
The success of attention in addressing alignment in machine translation gave rise to the idea of a sole attention-based architecture called Transformer [21]. The Transformer architecture produced even better results in neural machine translation. More importantly, it has become state-of-the-art solution in language modeling and started to be used as a pre-trained language model. The use of it as a pre-trained language model and the transfer of this model’s knowledge to other models introduced performance boost in a wide variety of NLP tasks.
The contribution of attention is not limited to the performance boost introduced but is also related to supporting explainability in deep learning. The visualization of attention provides a clue to the implicit features learned for the task at hand.
To observe the performance of the developed methods on computer vision problems, several competitions are arranged all around the world. One of them is Large Scale Visual Recognition Challenge [22]. This event contains several tasks which are image classification, object detection, and object localization. In image classification task, the aim is to predict the class of images in the test set given a set of discrete labels, such as dog, cat, truck, plane, etc. This is not a trivial task since different images of the same class have quite different instances and varying viewpoints, illumination, deformation, occlusion, etc.
All competitors in ILSVRC train their model on ImageNet [22] dataset. ImageNet 2012 dataset contains 1.2 million images and 1000 classes. Classification performances of proposed methods were compared according to two different evaluation criteria which are top 1 and top 5 score. In top 5 criterion, for each image top 5 guesses of the algorithm are considered. If actual image category is one of these five labels, then the image is counted as correctly classified. Total number of incorrect answers in this sense is called top 5 error.
An outstanding performance was observed by a CNN (convolutional neural network) in 2012. AlexNet [23] got the first place in classification task achieving 16.4% error rate. There was a huge difference between the first (16.4%) and second place (26.1%). In ILSVRC 2014, GoogleNet [24] took the first place achieving 6.67% error rate. Positive effect of network depth was observed. One year later, ResNet took the first place achieving 3.6% error rate [25] with a CNN of 152 layers. In the following years, even lower error rates were achieved with several modifications. Please note that the human performance on the image classification task was reported to be 5.1% error [22].
CNNs are the fundamental structures while working on images and videos. A typical CNN is actually composed of several layers interleaved with each other.
Convolutional layer is the core building block of a CNN. It contains plenty of learnable filters (or kernels). Each filter is convolved across width and height of input images. At the end of training process, filters of network are able to identify specific types of appearances (or patterns). A mathematical example is given to illustrate how convolutional layers work (Figure 9). In this example, a 5 × 5 RGB image is given to the network. Since images are represented as 3D arrays of numbers, input consists of three matrices. It is convolved with a filter of size 3 × 3 × 3 (height, weight, and depth). In this example, convolution is applied by moving the filter one pixel at a time, i.e., stride size = 1. First convolution operation can be seen at Figure 9a. After moving the kernel one pixel to the right, second convolution operation can be seen at Figure 9b. Element-wise multiplication
Convolution process. (a) First convolution operation applied with filter W1. Computation gives us the top-left member of an activation map in the next layer. (b) Second convolution operation, again applied with filter W1.
Convolution depicted in Figure 9 is performed with one filter which results in one matrix (called activation map) in the convolution layer. Using
Formation of a convolution layer by applying n number of learnable filters on the previous layer. Each activation map is formed by convolving a different filter on the whole input. In this example input to the convolution is the RGB image itself (depth = 3). For every further layer, input is its previous layer. After convolution, width and height of the next layer may or may not decrease.
Pooling layer is commonly used between convolutional layers to reduce the number of parameters in the upcoming layers. It makes the representations smaller and the algorithm much faster. With max pooling, filter takes the largest number in the region covered by the matrix on which it is applied. Example input, on which 2 × 2 max pooling is applied, is shown in Figure 11. If the input size is
Max pooling.
Standard CNNs generally have several convolution layers, followed by pooling layers and at the end a few fully connected layers (Figure 12). CNNs are similar to standard neural networks, but instead of connecting weights to all units of the previous layer, a convolution operation is applied on the units (voxels) of the previous layer. It enables us scale weights in an efficient way since a filter has a fixed number of weights and it is independent of the number of the voxels in the previous layer.
A typical CNN for image classification task.
What we have in the last fully connected layer of a classification network is the output scores for each class. It may seem trivial to select the class with the highest score to make a decision; however we need to define a loss to be able to train the network. Loss is defined according to the scores obtained for the classes. A common practice is to use softmax function, which first converts the class scores into normalized probabilities (Eq. (10)):
where
An example of softmax classification loss calculation. Computed loss, Li, is only for the ith sample in the dataset.
The ability of a model to make correct predictions for new samples after trained on the training set is defined as generalization. Thus, we would like to train a CNN with a high generalization capacity. Its high accuracy should not be only for training samples. In general, we should increase the size and variety of the training data, and we should avoid training an excessively complex model (simply called overfitting). Since it is not always easy to obtain more training data and to pick the best complexity for our model, let’s discuss a few popular techniques to increase the generalization capacity.
This is a term,
Another way to prevent overfitting is a technique called dropout, which corresponds to removing some units in the network [26]. The neurons which are “dropped out” in this way do not contribute to the forward pass (computation of loss for a given input) and do not participate in backpropagation (Figure 14). In each forward pass, a random set of neurons are dropped (with a hyperparameter of dropping probability, usually 0.5).
Applying dropout in a neural net.
The more training samples for a model, the more successful the model will be. However, it is rarely possible to obtain large-size datasets either because it is hard to collect more samples or it is expensive to annotate large number of samples. Therefore, to increase the size of existing raw data, producing synthetic data is sometimes preferred. For visual data, data size can be increased by rotating the picture at different angles, random translations, rotations, crops, flips, or altering brightness and contrast [27].
Short after people realized that CNNs are very powerful nonlinear models for computer vision problems, they started to seek an insight of why these models perform so well. To this aim, researchers proposed visualization techniques that provide an understanding of what features are learned in different layers of a CNN [28]. It turns out that first convolutional layers are responsible for learning low-level features (edges, lines, etc.), whereas as we go further in the convolutional layers, specific shapes and even distinctive patterns can be learned (Figure 15).
Image patches corresponding to the highest activations in a random subset of feature maps. First layer’s high activations occur at patches of distinct low-level features such as edges (a) and lines (b); further layers’ neurons learn to fire at more complex structures such as geometric shapes (c) or patterns on an animal (d). Since activations in the first layer correspond to small areas on images, resolution of patches in (a) and (b) is low.
In early days of observing the great performance of CNNs, it was believed that one needs a very large dataset in order to use CNNs. Later, it was discovered that, since the pre-trained models already learned to distinguish some patterns, they provide great benefits for new problems and new datasets from varying domains. Transfer learning is the name of training a new model with transferring weights from a related model that had already been trained.
If the dataset in our new task is small but similar to the one that was used in pre-trained model, then it would work to change the classification layer (according to our classes) and train this last layer. However, if our dataset is also big enough, we can include a few more layers (starting from the fully connected layers at the end) to our retraining scheme, which is also called fine-tuning. For instance, if a face recognition model trained with a large database is available and you would like to use that model with the faces in your company, that would constitute an ideal case of transferring the weights from the pre-trained model and fine-tune one or two layers with your local database. On the other hand, if the dataset in our new task is not similar to the one used in pre-trained model, then we would need a larger dataset and need to retrain a larger number of layers. An example of this case is learning to classify CT (computer tomography) images using a CNN pre-trained on ImageNet dataset. In this situation, the complex patterns (cf. Figure 15c and d) that were learned within the pre-trained model are not much useful for your new task. If both the new dataset is small and images are much different from those of a trained model, then users should not expect any benefit from transferring weights. In such cases users should find a way to enlarge the dataset and train a CNN from scratch using the newly collected training data. The cases that a practitioner may encounter from the transfer learning point of view are summarized in Table 1.
Very similar dataset | Very different dataset | |
---|---|---|
Very little data | Replace the classification layer | Not recommended |
A lot of data | Fine-tune a few layers | Fine-tune a larger number of layers |
Strategies of transfer learning according to the size of the new dataset and its similarity to the one used in pre-trained model.
To emphasize the importance of transfer learning, let us present a small experiment where the same model is trained with and without transfer learning. Our task is the classification of animals (four classes) from their images. Classes are zebra, leopard, elephant, and bear where each class has 350 images collected from the Internet (Figure 16). Transfer learning is performed using an AlexNet [23] pre-trained on ImageNet dataset. We have replaced the classification layer with a four-neuron layer (one for each class) which was originally 1000 (number of classes in ImageNet). In training conducted with transfer learning, we reached a 98.81% accuracy on the validation set after five epochs (means after seeing the dataset five times during training). Readers can observe that accuracy is quite satisfactory even after one epoch (Figure 17a). On the other hand, in training without transfer learning, we could reach only 76.90% accuracy even after 40 epochs (Figure 17b). Trying different hyperparameters (regularization strength, learning rate, etc.) could have a chance to increase accuracy a little bit more, but this does not alleviate the importance of applying transfer learning.
Example images for each class used in the experiment of transfer learning for animal classification.
Training and validation set accuracies obtained (a) with transfer learning and (b) without transfer learning.
Deep learning has become the dominant machine learning approach due to the availability of vast amounts of data and improved computational resources. The main transformation was observed in text and image analysis.
In NLP, change can be described in two major lines. The first line is learning better representations through ever-improving neural language models. Currently, self-attention-based Transformer language model is state-of-the-art, and learned representations are capable to capture a mix of syntactic and semantic features and are context-dependent. The second line is related to neural network solutions in different NLP tasks. Although LSTMs proved useful in capturing long-term dependencies in the nature of temporal data, the recent trend has been to transfer the pre-trained language models’ knowledge into fine-tuned task-specific models. Self-attention neural network mechanism has become the dominant scheme in pre-trained language models. This transfer learning solution outperformed existing approaches in a significant way.
In the field of computer vision, CNNs are the best performing solutions. There are very deep CNN architectures that are fine-tuned, thanks to huge amounts of training data. The use of pre-trained models in different vision tasks is a common methodology as well.
One common disadvantage of deep learning solutions is the lack of insights due to learning implicitly. Thus, attention mechanism together with visualization seems promising in both NLP and vision tasks. The fields are in the quest of more explainable solutions.
One final remark is on the rise of multimodal solutions. Till now question answering has been an intersection point. Future work are expected to be devoted to multimodal solutions.
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\n\n4.1 The Corresponding Author represents and warrants that the Chapter does not and will not breach any applicable law or the rights of any third party and, specifically, that the Chapter contains no matter that is defamatory or that infringes any literary or proprietary rights, intellectual property rights, or any rights of privacy. The Corresponding Author warrants and represents that: (i) the Chapter is the original work of themselves and any Co-Author and is not copied wholly or substantially from any other work or material or any other source; (ii) the Chapter has not been formally published in any other peer-reviewed journal or in a book or edited collection, and is not under consideration for any such publication; (iii) they themselves and any Co-Author are qualifying persons under section 154 of the Copyright, Designs and Patents Act 1988; (iv) they themselves and any Co-Author have not assigned and will not during the term of this Publication Agreement purport to assign any of the rights granted to IntechOpen under this Publication Agreement; and (v) the rights granted by this Publication Agreement are free from any security interest, option, mortgage, charge or lien.
\n\nThe Corresponding Author also warrants and represents that: (i) they have the full power to enter into this Publication Agreement on their own behalf and on behalf of each Co-Author; and (ii) they have the necessary rights and/or title in and to the Chapter to grant IntechOpen, on behalf of themselves and any Co-Author, the rights and licenses expressed to be granted in this Publication Agreement. If the Chapter was prepared jointly by the Corresponding Author and any Co-Author, the Corresponding Author warrants and represents that: (i) each Co-Author agrees to the submission, license and publication of the Chapter on the terms of this Publication Agreement; and (ii) they have the authority to enter into this Publication Agreement on behalf of and bind each Co-Author. The Corresponding Author shall: (i) ensure each Co-Author complies with all relevant provisions of this Publication Agreement, including those relating to confidentiality, performance and standards, as if a party to this Publication Agreement; and (ii) remain primarily liable for all acts and/or omissions of each such Co-Author.
\n\nThe Corresponding Author agrees to indemnify and hold IntechOpen harmless against all liabilities, costs, expenses, damages and losses and all reasonable legal costs and expenses suffered or incurred by IntechOpen arising out of or in connection with any breach of the aforementioned representations and warranties. This indemnity shall not cover IntechOpen to the extent that a claim under it results from IntechOpen's negligence or willful misconduct.
\n\n4.2 Nothing in this Publication Agreement shall have the effect of excluding or limiting any liability for death or personal injury caused by negligence or any other liability that cannot be excluded or limited by applicable law.
\n\n5. TERMINATION
\n\n5.1 IntechOpen has a right to terminate this Publication Agreement for quality, program, technical or other reasons with immediate effect, including without limitation (i) if the Corresponding Author or any Co-Author commits a material breach of this Publication Agreement; (ii) if the Corresponding Author or any Co-Author (being an individual) is the subject of a bankruptcy petition, application or order; or (iii) if the Corresponding Author or any Co-Author (being a company) commences negotiations with all or any class of its creditors with a view to rescheduling any of its debts, or makes a proposal for or enters into any compromise or arrangement with any of its creditors.
\n\nIn case of termination, IntechOpen will notify the Corresponding Author, in writing, of the decision.
\n\n6. INTECHOPEN’S DUTIES AND RIGHTS
\n\n6.1 Unless prevented from doing so by events outside its reasonable control, IntechOpen, in its discretion, agrees to publish the Chapter attributing it to the Corresponding Author and any Co-Author.
\n\n6.2 IntechOpen has the right to use the Corresponding Author’s and any Co-Author’s names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Chapter and has the right to contact the Corresponding Author and any Co-Author until the Chapter is publicly available on any platform owned and/or operated by IntechOpen.
\n\n6.3 IntechOpen is granted the authority to enforce the rights from this Publication Agreement, on behalf of the Corresponding Author and any Co-Author, against third parties (for example in cases of plagiarism or copyright infringements). In respect of any such infringement or suspected infringement of the copyright in the Chapter, IntechOpen shall have absolute discretion in addressing any such infringement which is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
\n\n7. MISCELLANEOUS
\n\n7.1 Further Assurance: The Corresponding Author shall and will ensure that any relevant third party (including any Co-Author) shall, execute and deliver whatever further documents or deeds and perform such acts as IntechOpen reasonably requires from time to time for the purpose of giving IntechOpen the full benefit of the provisions of this Publication Agreement.
\n\n7.2 Third Party Rights: A person who is not a party to this Publication Agreement may not enforce any of its provisions under the Contracts (Rights of Third Parties) Act 1999.
\n\n7.3 Entire Agreement: This Publication Agreement constitutes the entire agreement between the parties in relation to its subject matter. It replaces and extinguishes all prior agreements, draft agreements, arrangements, collateral warranties, collateral contracts, statements, assurances, representations and undertakings of any nature made by or on behalf of the parties, whether oral or written, in relation to that subject matter. Each party acknowledges that in entering into this Publication Agreement it has not relied upon any oral or written statements, collateral or other warranties, assurances, representations or undertakings which were made by or on behalf of the other party in relation to the subject matter of this Publication Agreement at any time before its signature (together "Pre-Contractual Statements"), other than those which are set out in this Publication Agreement. Each party hereby waives all rights and remedies which might otherwise be available to it in relation to such Pre-Contractual Statements. Nothing in this clause shall exclude or restrict the liability of either party arising out of its pre-contract fraudulent misrepresentation or fraudulent concealment.
\n\n7.4 Waiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
\n\n7.5 Variation: No variation of this Publication Agreement shall be effective unless it is in writing and signed by the parties (or their duly authorized representatives).
\n\n7.6 Severance: If any provision or part-provision of this Publication Agreement is or becomes invalid, illegal or unenforceable, it shall be deemed modified to the minimum extent necessary to make it valid, legal and enforceable. If such modification is not possible, the relevant provision or part-provision shall be deemed deleted.
\n\nAny modification to or deletion of a provision or part-provision under this clause shall not affect the validity and enforceability of the rest of this Publication Agreement.
\n\n7.7 No partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Corresponding Author or any Co-Author, nor authorize any party to make or enter into any commitments for or on behalf of any other party.
\n\n7.8 Governing law: This Publication Agreement and any dispute or claim (including non-contractual disputes or claims) arising out of or in connection with it or its subject matter or formation shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of or in connection with this Publication Agreement (including any non-contractual disputes or claims).
\n\nLast updated: 2020-11-27
\n\n\n\n
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