More importantly, we employ a generative model for synthetic samples to simulate the intervention of various non-causal effects, thereby minimizing their impact on hash codes for domain invariance. Comprehensive experiments conducted on benchmark datasets confirm the superior performance of our proposed IDEA compared to a variety of competitive baselines.

In this study, we delve into the task of few-shot Generative Domain Adaptation (GDA), which involves transferring a pre-trained generator from one domain to a new domain using only a few reference images. Inspired by the way human brains acquire knowledge in new domains, we present an innovative generator structure called Domain Re-Modulation (DoRM) .

To answer this question, we begin by revisiting the forward procedure of ViTs. A sequence of positional embeddings (PEs) [51] is added to patch embeddings to preserve position information. Intuitively, simply discarding these PEs and requesting the model to reconstruct the position for each patch naturally becomes a qualified location-aware pretext task.