Zero-shot domain adaptation based on dual-level mix and contrast
<|reference_start|>Zero-shot domain adaptation based on dual-level mix and contrast: 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.<|reference_end|>
@article{zhe2024zero-shot,
    title={Zero-shot domain adaptation based on dual-level mix and contrast},
    author={Yu Zhe, Jun Sakuma},
    journal={arXiv preprint arXiv:2406.18996},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18996},
    primaryClass={cs.CV cs.LG}
}