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Learning from a Complementary-label Source Domain: Theory and Algorithms
In unsupervised domain adaptation (UDA), a classifier for the target dom...
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Low-Budget Unsupervised Label Query through Domain Alignment Enforcement
Deep learning revolution happened thanks to the availability of a massiv...
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Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation
In unsupervised domain adaptation (UDA), classifiers for the target doma...
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Improved Mutual Mean-Teaching for Unsupervised Domain Adaptive Re-ID
In this technical report, we present our submission to the VisDA Challen...
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Understanding Self-Training for Gradual Domain Adaptation
Machine learning systems must adapt to data distributions that evolve ov...
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Parameter Reference Loss for Unsupervised Domain Adaptation
The success of deep learning in computer vision is mainly attributed to ...
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Domain Adaptive Ensemble Learning
The problem of generalizing deep neural networks from multiple source do...
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Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation
In unsupervised domain adaptation (UDA), classifiers for the target domain are trained with massive true-label data from the source domain and unlabeled data from the target domain. However, it may be difficult to collect fully-true-label data in a source domain given a limited budget. To mitigate this problem, we consider a novel problem setting where the classifier for the target domain has to be trained with complementary-label data from the source domain and unlabeled data from the target domain named budget-friendly UDA (BFUDA). The key benefit is that it is much less costly to collect complementary-label source data (required by BFUDA) than collecting the true-label source data (required by ordinary UDA). To this end, the complementary label adversarial network (CLARINET) is proposed to solve the BFUDA problem. CLARINET maintains two deep networks simultaneously, where one focuses on classifying complementary-label source data and the other takes care of the source-to-target distributional adaptation. Experiments show that CLARINET significantly outperforms a series of competent baselines.
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