scholarly journals Domain-Specific Batch Normalization for Unsupervised Domain Adaptation

2019 ◽  
Author(s):  
Woong-Gi Chang ◽  
Tackgeun You ◽  
Seonguk Seo ◽  
Suha Kwak ◽  
Bohyung Han
Keyword(s):  
Domain Adaptation ◽  
Domain Specific ◽  
Unsupervised Domain Adaptation ◽  
Batch Normalization

scholarly journals Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling

2020 ◽  
Vol 34 (04) ◽  
pp. 6243-6250 ◽  
Author(s):  
Qian Wang ◽  
Toby Breckon
Keyword(s):  
Domain Adaptation ◽  
Feature Space ◽  
Structured Prediction ◽  
Target Domain ◽  
Source Domain ◽  
Domain Specific ◽  
Unsupervised Domain Adaptation ◽  
Deep Feature ◽  
Significant Performance ◽  
Error Accumulation

Unsupervised domain adaptation aims to address the problem of classifying unlabeled samples from the target domain whilst labeled samples are only available from the source domain and the data distributions are different in these two domains. As a result, classifiers trained from labeled samples in the source domain suffer from significant performance drop when directly applied to the samples from the target domain. To address this issue, different approaches have been proposed to learn domain-invariant features or domain-specific classifiers. In either case, the lack of labeled samples in the target domain can be an issue which is usually overcome by pseudo-labeling. Inaccurate pseudo-labeling, however, could result in catastrophic error accumulation during learning. In this paper, we propose a novel selective pseudo-labeling strategy based on structured prediction. The idea of structured prediction is inspired by the fact that samples in the target domain are well clustered within the deep feature space so that unsupervised clustering analysis can be used to facilitate accurate pseudo-labeling. Experimental results on four datasets (i.e. Office-Caltech, Office31, ImageCLEF-DA and Office-Home) validate our approach outperforms contemporary state-of-the-art methods.

scholarly journals Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources

2019 ◽  
Vol 33 ◽  
pp. 5989-5996 ◽  
Author(s):  
Yongchun Zhu ◽  
Fuzhen Zhuang ◽  
Deqing Wang
Keyword(s):  
Domain Adaptation ◽  
Feature Space ◽  
Multiple Sources ◽  
Target Domain ◽  
Domain Specific ◽  
Unsupervised Domain Adaptation ◽  
Specific Distribution ◽  
Benchmark Datasets ◽  
Invariant Representations ◽  
Decision Boundaries

While Unsupervised Domain Adaptation (UDA) algorithms, i.e., there are only labeled data from source domains, have been actively studied in recent years, most algorithms and theoretical results focus on Single-source Unsupervised Domain Adaptation (SUDA). However, in the practical scenario, labeled data can be typically collected from multiple diverse sources, and they might be different not only from the target domain but also from each other. Thus, domain adapters from multiple sources should not be modeled in the same way. Recent deep learning based Multi-source Unsupervised Domain Adaptation (MUDA) algorithms focus on extracting common domain-invariant representations for all domains by aligning distribution of all pairs of source and target domains in a common feature space. However, it is often very hard to extract the same domain-invariant representations for all domains in MUDA. In addition, these methods match distributions without considering domain-specific decision boundaries between classes. To solve these problems, we propose a new framework with two alignment stages for MUDA which not only respectively aligns the distributions of each pair of source and target domains in multiple specific feature spaces, but also aligns the outputs of classifiers by utilizing the domainspecific decision boundaries. Extensive experiments demonstrate that our method can achieve remarkable results on popular benchmark datasets for image classification.

scholarly journals Improving Domain-Specific Classification by Collaborative Learning with Adaptation Networks

2019 ◽  
Vol 33 ◽  
pp. 5450-5457 ◽  
Author(s):  
Si Wu ◽  
Jian Zhong ◽  
Wenming Cao ◽  
Rui Li ◽  
Zhiwen Yu ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Visual Adaptation ◽  
Generalization Capability ◽  
Target Domain ◽  
Domain Specific ◽  
Unsupervised Domain Adaptation ◽  
Source Data ◽  
Data Points ◽  
Invariant Representations ◽  
Target Data

For unsupervised domain adaptation, the process of learning domain-invariant representations could be dominated by the labeled source data, such that the specific characteristics of the target domain may be ignored. In order to improve the performance in inferring target labels, we propose a targetspecific network which is capable of learning collaboratively with a domain adaptation network, instead of directly minimizing domain discrepancy. A clustering regularization is also utilized to improve the generalization capability of the target-specific network by forcing target data points to be close to accumulated class centers. As this network learns and specializes to the target domain, its performance in inferring target labels improves, which in turn facilitates the learning process of the adaptation network. Therefore, there is a mutually beneficial relationship between these two networks. We perform extensive experiments on multiple digit and object datasets, and the effectiveness and superiority of the proposed approach is presented and verified on multiple visual adaptation benchmarks, e.g., we improve the state-ofthe-art on the task of MNIST→SVHN from 76.5% to 84.9% without specific augmentation.

Importance-weighted conditional adversarial network for unsupervised domain adaptation

2020 ◽  
Vol 155 ◽  
pp. 113404 ◽  
Author(s):  
Peng Liu ◽  
Ting Xiao ◽  
Cangning Fan ◽  
Wei Zhao ◽  
Xianglong Tang ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Unsupervised Domain Adaptation ◽  
Adversarial Network
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