  Source-free Domain Adaptation (SFDA) aims to adapt a pre-trained source model
to the unlabeled target domain without accessing the well-labeled source data,
which is a much more practical setting due to the data privacy, security, and
transmission issues. To make up for the absence of source data, most existing
methods introduced feature prototype based pseudo-labeling strategies to
realize self-training model adaptation. However, feature prototypes are
obtained by instance-level predictions based feature clustering, which is
category-biased and tends to result in noisy labels since the visual domain
gaps between source and target are usually different between categories. In
addition, we found that a monocentric feature prototype may be ineffective to
represent each category and introduce negative transfer, especially for those
hard-transfer data. To address these issues, we propose a general
class-Balanced Multicentric Dynamic prototype (BMD) strategy for the SFDA task.
Specifically, for each target category, we first introduce a global inter-class
balanced sampling strategy to aggregate potential representative target
samples. Then, we design an intra-class multicentric clustering strategy to
achieve more robust and representative prototypes generation. In contrast to
existing strategies that update the pseudo label at a fixed training period, we
further introduce a dynamic pseudo labeling strategy to incorporate network
update information during model adaptation. Extensive experiments show that the
proposed model-agnostic BMD strategy significantly improves representative SFDA
methods to yield new state-of-the-art results. The code is available at
https://github.com/ispc-lab/BMD.
