Neighborhood-Aware Mutual Information Maximization for Source-Free Domain Adaptation

Abstract

Recently, the source-free domain adaptation (SFDA) problem has attracted much attention, where the pre-trained model for the source domain is adapted to the target domain in the absence of source data. However, due to domain shift, the negative alignment usually exists between samples from the same class, which may lower intra-class feature similarity. To address this issue, we present a self-supervised representation learning strategy for SFDA, named as neighborhood-aware mutual information (NAMI), which maximizes the mutual information (MI) between the representations of target samples and their corresponding neighbors. Moreover, we theoretically demonstrate that NAMI can be decomposed into a weighted sum of local MI, which suggests that the weighted terms can better estimate NAMI. To this end, we introduce neighborhood consensus score over the set of weakly and strongly augmented views and point-wise density based on neighborhood, both of which determine the weights of local MI for NAMI by leveraging the neighborhood information of samples. The proposed method can significantly handle domain shift and adaptively reduce the noise in the neighborhood of each target sample. In combination with the consistency loss over views, NAMI leads to consistent improvement over existing state-of-the-art methods on three popular SFDA benchmarks.