Yalın Bastanlar

By Selma Tekir and Yalin Bastanlar

Deep learning has become the most popular approach in machine learning in recent years. The reason lies in considerably high accuracies obtained by deep learning methods in many tasks especially with textual and visual data. In fact, natural language processing (NLP) and computer vision are the two research areas that deep learning has demonstrated its impact at utmost level. This chapter will firstly summarize the historical evolution of deep neural networks and their fundamental working principles. After briefly introducing the natural language processing and computer vision research areas, it will explain how exactly deep learning is used to solve the problems in these two areas. Several examples regarding the common tasks of these research areas and some discussion are also provided.

Part of the book: Data Mining

By Yalin Bastanlar and Semih Orhan

Although its origins date a few decades back, contrastive learning has recently gained popularity due to its achievements in self-supervised learning, especially in computer vision. Supervised learning usually requires a decent amount of labeled data, which is not easy to obtain for many applications. With self-supervised learning, we can use inexpensive unlabeled data and achieve a training on a pretext task. Such a training helps us to learn powerful representations. In most cases, for a downstream task, self-supervised training is fine-tuned with the available amount of labeled data. In this study, we review common pretext and downstream tasks in computer vision and we present the latest self-supervised contrastive learning techniques, which are implemented as Siamese neural networks. Lastly, we present a case study where self-supervised contrastive learning was applied to learn representations of semantic masks of images. Performance was evaluated on an image retrieval task and results reveal that, in accordance with the findings in the literature, fine-tuning the self-supervised training showed the best performance.

Part of the book: Artificial Intelligence Annual Volume 2022