Rethinking Neighborhood Consistency Learning on Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) involves predicting unlabeled data in a target domain by using labeled data from the source domain. However, recent advances in pseudo-labeling (PL) methods have been hampered by noisy pseudo-labels that diminish the local discriminativeness of the target structure. Although neighborhood-based PL can help preserve the local structure, it also risks assigning the whole local neighborhood to the wrong semantic category. To address this issue, we propose a novel framework called neighborhood consistency learning (NCL) that operates at both the semantic and instance levels and features a new consistency objective function. Specifically, our objective function aims to promote semantic consistency in the target neighborhood by computing the correlation matrix between the target samples and their neighborhood aggregation over a batch and matching the correlation matrix to an identity matrix. Importantly, our approach allows the target neighborhood to receive gradients from several potential positive categories instead of just one certain category. Our extensive experiments on UDA benchmarks demonstrate the effectiveness of NCL over other state-of-the-art PL-based methods.