Target Consistency for Domain Adaptation: when Robustness meets Transferability

Abstract

Learning Invariant Representations has been successfully applied for reconciling a source and a target domain for Unsupervised Domain Adaptation. By investigating the robustness of such methods under the prism of the cluster assumption, we bring new evidence that invariance with a low source risk does not guarantee a wellperforming target classifier. More precisely, we show that the cluster assumption is violated in the target domain despite being maintained in the source domain, indicating a lack of robustness of the target classifier. To address this problem, we demonstrate the importance of enforcing the cluster assumption in the target domain, named Target Consistency (TC), especially when paired with Class-Level InVariance (CLIV). Our new approach results in a significant improvement, on both image classification and segmentation benchmarks, over state-of-the-art methods based on invariant representations. Importantly, our method is flexible and easy to implement, making it a complementary technique to existing approaches for improving transferability of representations. * Equal contribution Preprint. Under review.

Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift

“…Outstanding progress have been towards learning more domain transferable representations by looking for domain invariance. The tensorial product between representations and prediction promotes conditional domain invariance [41], the use of weights [10,62,7,14] has dramatically improved the problem of label shift theoretically described in [64], hallucinating consistent target samples [38], penalizing high singular values of batch of representations [12] or by enforcing the favorable inductive bias of consistence through various data augmentation in the target domain [45]. Recent works address the problem of adaptation without source data [37,61].…”