Abstract
Source-free domain adaptation requires no access to the source domain training data during unsupervised domain adaption. This is critical for meeting particular data sharing, privacy, and license constraints, whilst raising novel algorithmic challenges. Existing source-free domain adaptation methods rely on either generating pseudo samples/prototypes of source or target domain style, or simply leveraging pseudo-labels (self-training). They suffer from low-quality generated samples/prototypes or noisy pseudo-label target samples. In this work, we address both limitations by introducing a novel Class Prototype Discovery (CPD) method. In contrast to all alternatives, our CPD is established on a set of semantic class prototypes each constructed for representing a specific class. By designing a classification score based prototype learning mechanism, we reformulate the source-free domain adaptation problem to class prototype optimization using all the target domain training data, and without the need for data generation. Then, class prototypes are used to cluster target features to assign them pseudo-labels, which highly complements the conventional self-training strategy. Besides, a prototype regularization is introduced for exploiting well-established distribution alignment based on pseudo-labeled target samples and class prototypes. Along with theoretical analysis, we conduct extensive experiments on three standard benchmarks to validate the performance advantages of our CPD over the state-of-the-art models.
•We propose a novel Class Prototype Discovery method for solving the SFDA problem.•A prototype regularization is introduced based on distribution alignment strategy.•CPD outperforms a wide variety of state-of-the-art methods, often by a large margin.