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Deep-Learning Systems for Domain Adaptation in Computer Vision: Learning Transferable Feature Representations

机译：用于计算机视觉领域适应的深度学习系统：学习可转移的特征表示

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摘要

Domain adaptation algorithms address the issue of transferring learning across computational models to adapt them to data from different distributions. In recent years, research in domain adaptation has been making great progress owing to the advancements in deep learning. Deep neural networks have demonstrated unrivaled success across multiple computer vision applications, including transfer learning and domain adaptation. This article outlines the latest research in domain adaptation using deep neural networks. It begins with an introduction to the concept of knowledge transfer in machine learning and the different paradigms of transfer learning. It provides a brief survey of nondeep-learning techniques and organizes the rapidly growing research in domain adaptation based on deep learning. It also highlights some drawbacks with the current state of research in this area and offers directions for future research.
机译：域自适应算法解决了跨计算模型转移学习以使其适应来自不同分布的数据的问题。近年来，由于深度学习的进步，领域适应性研究取得了长足的进步。深度神经网络已在多种计算机视觉应用程序中证明了无与伦比的成功，包括转移学习和领域适应。本文概述了使用深度神经网络进行领域自适应的最新研究。它首先介绍了机器学习中知识转移的概念以及转移学习的不同范例。它简要介绍了非深度学习技术，并组织了基于深度学习的领域适应快速增长的研究。它还强调了该领域当前研究现状的一些弊端，并为将来的研究提供了方向。

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