Deep Visual Domain Adaptation
This survey paper helps researchers and practitioners in computer vision understand the landscape of deep domain adaptation methods, addressing the challenge of transferring knowledge across different but related domains for deep learning models.
This paper provides a comprehensive overview of deep domain adaptation methods for computer vision applications, detailing and comparing different approaches to exploit deep architectures for domain adaptation. It also reviews recent trends and improvement strategies applicable to these models, primarily focusing on image classification.
Domain adaptation (DA) aims at improving the performance of a model on target domains by transferring the knowledge contained in different but related source domains. With recent advances in deep learning models which are extremely data hungry, the interest for visual DA has significantly increased in the last decade and the number of related work in the field exploded. The aim of this paper, therefore, is to give a comprehensive overview of deep domain adaptation methods for computer vision applications. First, we detail and compared different possible ways of exploiting deep architectures for domain adaptation. Then, we propose an overview of recent trends in deep visual DA. Finally, we mention a few improvement strategies, orthogonal to these methods, that can be applied to these models. While we mainly focus on image classification, we give pointers to papers that extend these ideas for other applications such as semantic segmentation, object detection, person re-identifications, and others.