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Title: Unsupervised domain adaptation with robust deep logistic regression
Authors: Wu, G 
Chen, W
Zuo, W
Zhang, D 
Keywords: Deep convolutional networks
Domain adaptation
Robust logistic regression
Semi-supervised learning
Issue Date: 2018
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2018, v. 10749, p. 199-211 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: The goal of unsupervised domain adaptation (UDA) is to eliminate the cross-domain discrepancy in probability distributions without the availability of labeled target samples during training. Even recent studies have revealed the benefit of deep convolutional features trained on a large set (e.g., ImageNet) in alleviating domain discrepancy. The transferability of features decreases as (i) the difference between the source and target domains increases, or (ii) the layers are toward the top layer. Therefore, even with deep features, domain adaptation remains necessary. In this paper, we treat UDA as a special case of semi-supervised learning, where the source samples are labeled while the target samples are unlabeled. Conventional semi-supervised learning methods, however, usually attain poor performance for UDA. Due to domain discrepancy, label noise generally is inevitable when using the classifiers trained on source classifier to predict target samples. Thus we deploy a robust deep logistic regression loss on the target samples, resulting in our RDLR model. In such a way, pseudo-labels are gradually assigned to unlabeled target samples according to their maximum classification scores during training. Extensive experiments show that our method yields the state-of-the-art results, demonstrating the effectiveness of robust logistic regression classifiers in UDA.
Description: 8th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2017, Wuhan, China, 20-24 Nov 2017
ISBN: 9.78332E+12
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-75786-5_17
Appears in Collections:Conference Paper

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