Zero-shot domain adaptation based on dual-level mix and contrast

• URL: http://arxiv.org/abs/2406.18996v1
• Date: Thu, 27 Jun 2024 08:37:26 GMT
• Title: Zero-shot domain adaptation based on dual-level mix and contrast
• Authors: Yu Zhe, Jun Sakuma,
• Abstract summary: This paper proposes a new ZSDA method to learn domain-invariant features with low task bias. Experimental results show that our proposal achieves good performance on several benchmarks.
• Score: 8.225819874406238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-shot domain adaptation (ZSDA) is a domain adaptation problem in the situation that labeled samples for a target task (task of interest) are only available from the source domain at training time, but for a task different from the task of interest (irrelevant task), labeled samples are available from both source and target domains. In this situation, classical domain adaptation techniques can only learn domain-invariant features in the irrelevant task. However, due to the difference in sample distribution between the two tasks, domain-invariant features learned in the irrelevant task are biased and not necessarily domain-invariant in the task of interest. To solve this problem, this paper proposes a new ZSDA method to learn domain-invariant features with low task bias. To this end, we propose (1) data augmentation with dual-level mixups in both task and domain to fill the absence of target task-of-interest data, (2) an extension of domain adversarial learning to learn domain-invariant features with less task bias, and (3) a new dual-level contrastive learning method that enhances domain-invariance and less task biasedness of features. Experimental results show that our proposal achieves good performance on several benchmarks.

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