  Source-Free Domain Adaptation (SFDA) aims to solve the domain adaptation
problem by transferring the knowledge learned from a pre-trained source model
to an unseen target domain. Most existing methods assign pseudo-labels to the
target data by generating feature prototypes. However, due to the discrepancy
in the data distribution between the source domain and the target domain and
category imbalance in the target domain, there are severe class biases in the
generated feature prototypes and noisy pseudo-labels. Besides, the data
structure of the target domain is often ignored, which is crucial for
clustering. In this paper, a novel framework named PCSR is proposed to tackle
SFDA via a novel intra-class Polycentric Clustering and Structural
Regularization strategy. Firstly, an inter-class balanced sampling strategy is
proposed to generate representative feature prototypes for each class.
Furthermore, k-means clustering is introduced to generate multiple clustering
centers for each class in the target domain to obtain robust pseudo-labels.
Finally, to enhance the model's generalization, structural regularization is
introduced for the target domain. Extensive experiments on three UDA benchmark
datasets show that our method performs better or similarly against the other
state of the art methods, demonstrating our approach's superiority for visual
domain adaptation problems.
