计算机科学
适应(眼睛)
分类
域适应
领域(数学分析)
人工智能
学习迁移
卷积神经网络
同种类的
深度学习
分割
对象(语法)
机器学习
人机交互
心理学
数学
数学分析
组合数学
分类器(UML)
神经科学
出处
期刊:Cornell University - arXiv
日期:2017-02-17
被引量:144
摘要
The aim of this paper is to give an overview of domain adaptation and transfer learning with a specific view on visual applications. After a general motivation, we first position domain adaptation in the larger transfer learning problem. Second, we try to address and analyze briefly the state-of-the-art methods for different types of scenarios, first describing the historical shallow methods, addressing both the homogeneous and the heterogeneous domain adaptation methods. Third, we discuss the effect of the success of deep convolutional architectures which led to new type of domain adaptation methods that integrate the adaptation within the deep architecture. Fourth, we overview the methods that go beyond image categorization, such as object detection or image segmentation, video analyses or learning visual attributes. Finally, we conclude the paper with a section where we relate domain adaptation to other machine learning solutions.
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