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.

该论文提出了一种新的零样本领域自适应方法，通过双层混合增强和对抗学习来学习具有低任务偏差的领域不变特征，实验证明该方法在多个基准测试上取得了良好的性能。