Compact Feature Learning for Multi-Domain Image Classification

Yajing Liu, Xinmei Tian, Ya Li, Zhiwei Xiong, Feng Wu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 7193-7201

Abstract


The goal of multi-domain learning is to improve the performance over multiple domains by making full use of all training data from them. However, variations of feature distributions across different domains result in a non-trivial solution of multi-domain learning. The state-of-the-art work regarding multi-domain classification aims to extract domain-invariant features and domain-specific features independently. However, they view the distributions of features from different classes as a general distribution and try to match these distributions across domains, which lead to the mixture of features from different classes across domains and degrade the performance of classification. Additionally, existing works only force the shared features among domains to be orthogonal to the features in the domain-specific network. However, redundant features between the domain-specific networks still remain, which may shrink the discriminative ability of domain-specific features. Therefore, we propose an end-to-end network to obtain the more optimal features, which we call compact features. We propose to extract the domain-invariant features by matching the joint distributions of different domains, which have dis- tinct boundaries between different classes. Moreover, we add an orthogonal constraint between the private features across domains to ensure the discriminative ability of the domain-specific space. The proposed method is validated on three landmark datasets, and the results demonstrate the effectiveness of our method.

Related Material


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[bibtex]
@InProceedings{Liu_2019_CVPR,
author = {Liu, Yajing and Tian, Xinmei and Li, Ya and Xiong, Zhiwei and Wu, Feng},
title = {Compact Feature Learning for Multi-Domain Image Classification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}