  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.
