Multi-representation adaptation network for cross-domain image classification
Multi-representation adaptation network for cross-domain image classification
复制标题
用于跨域图像分类的多表示自适应网络
DOI：
10.1016/j.neunet.2019.07.010
复制
发表时间：
2019-11-01
期刊：
影响因子：
7.8
通讯作者：
He, Qing
中科院分区：
文献类型：
--
作者：
Zhu, Yongchun;Zhuang, Fuzhen;He, Qing
在图像分类中，获取足够的标签往往既昂贵又耗时。为了解决这个问题，领域自适应通常提供了一个有吸引力的选择，因为有大量来自相似性质但不同领域的标记数据。现有的方法主要是对齐由单一结构提取的表示的分布，并且表示可能只包含部分信息，例如只包含部分饱和度、亮度和色调信息。在此基础上，我们提出了多表征自适应方法，该方法可以显著提高跨域图像分类的分类精度，并专门针对由一个名为Inception Adaptation Module （IAM）的混合结构提取的多个表征的分布进行对齐。在此基础上，我们提出了多表示自适应网络（MRAN），通过多表示对齐来完成跨域图像分类任务，该网络可以捕获不同方面的信息。此外，我们扩展了最大平均差异（MMD）来计算自适应损失。我们的方法可以通过使用IAM扩展大多数前馈模型来轻松实现，并且可以通过反向传播有效地训练网络。在三个基准图像数据集上进行的实验证明了MRAN的有效性。(C) 2019 Elsevier Ltd.版权所有。
In image classification, it is often expensive and time-consuming to acquire sufficient labels. To solve this problem, domain adaptation often provides an attractive option given a large amount of labeled data from a similar nature but different domains. Existing approaches mainly align the distributions of representations extracted by a single structure and the representations may only contain partial information, e.g., only contain part of the saturation, brightness, and hue information. Along this line, we propose Multi-Representation Adaptation which can dramatically improve the classification accuracy for cross-domain image classification and specially aims to align the distributions of multiple representations extracted by a hybrid structure named Inception Adaptation Module (IAM). Based on this, we present Multi-Representation Adaptation Network (MRAN) to accomplish the cross-domain image classification task via multi-representation alignment which can capture the information from different aspects. In addition, we extend Maximum Mean Discrepancy (MMD) to compute the adaptation loss. Our approach can be easily implemented by extending most feed-forward models with IAM, and the network can be trained efficiently via back-propagation. Experiments conducted on three benchmark image datasets demonstrate the effectiveness of MRAN. (C) 2019 Elsevier Ltd. All rights reserved.