论文标题
深层视觉域的适应
Deep Visual Domain Adaptation
论文作者
论文摘要
域的适应性（DA）旨在通过转移不同但相关的源域中包含的知识来提高目标域模型的性能。随着深度学习模型的最新进展，这些模型极为饥饿，对视觉DA的兴趣在过去十年中显着增加，并且现场的相关工作数量爆炸。因此，本文的目的是全面概述用于计算机视觉应用的深层域适应方法。首先，我们详细介绍并比较了利用深层体系结构进行域适应的不同可能的方法。然后，我们提出了深度视觉DA的最新趋势的概述。最后，我们提到了一些改进策略，这些策略与这些方法正交，可以应用于这些模型。虽然我们主要关注图像分类，但我们向论文提供了将这些想法扩展到其他应用程序（例如语义细分，对象检测，人重新识别等）的论文。
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.
