Discriminative Pattern Calibration Mechanism for Source-Free Domain Adaptation

Abstract

Source-free domain adaptation (SFDA) assumes that model adaptation only accesses the well-learned source model and unlabeled target instances for knowledge transfer. However cross-domain distribution shift easily triggers invalid discriminative semantics from source model on recognizing the target samples. Hence understanding the specific content of discriminative pattern and adjusting their representation in target domain become the important key to overcome SFDA. To achieve such a vision this paper proposes a novel explanation paradigm "Discriminative Pattern Calibration (DPC)" mechanism on solving SFDA issue. Concretely DPC first utilizes learning network to infer the discriminative regions on the target images and specifically emphasizes them in feature space to enhance their representation. Moreover DPC relies on the attention-reversed mixup mechanism to augment more samples and improve the robustness of the classifier. Considerable experimental results and studies suggest that the effectiveness of our DPC in enhancing the performance of existing SFDA baselines.

Tasks

Reproductions