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Overcoming Data Inequality across Domains with Semi-Supervised Domain Generalization
In this paper, we address a representative case of data inequality problem across domains termed Semi-Supervised Domain Generalization (SSDG), in which only one domain is labeled while the rest are unlabeled.
Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning
In this paper, we propose Complementary Domain Adaptation and Generalization (CoDAG), a simple yet effective learning framework that combines domain adaptation and generalization in a complementary manner to achieve three major goals of unsupervised continual domain shift learning: adapting to a current domain, generalizing to unseen domains, and preventing forgetting of previously seen domains.
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Unsupervised Continual Domain Shift Learning
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Domain Generalization
Unsupervised Continual Domain Shift Learning
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