Cleaning Noisy Labels by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation

Abstract

Conventional Unsupervised Domain Adaptation (UDA) methods presume source and target domain data to be simultaneously available during training. Such an assumption may not hold in practice, as source data is often inaccessible (e.g., due to privacy reasons). On the contrary, a pre-trained source model is usually available, which performs poorly on target due to the well-known domain shift problem. This translates into a significant amount of misclassifications, which can be interpreted as structured noise affecting the inferred target pseudo-labels. In this work, we cast UDA as a pseudo-label refinery problem in the challenging source-free scenario. We propose Negative Ensemble Learning (NEL) technique, a unified method for adaptive noise filtering and progressive pseudo-label refinement. NEL is devised to tackle noisy pseudo-labels by enhancing diversity in ensemble members with different stochastic (i) input augmentation and (ii) feedback. The latter is achieved by leveraging the novel concept of Disjoint Residual Labels, which allow propagating diverse information to the different members. Eventually, a single model is trained with the refined pseudo-labels, which leads to a robust performance on the target domain. Extensive experiments show that the proposed method achieves state-of-the-art performance on major UDA benchmarks, such as Digit5, PACS, Visda-C, and DomainNet, without using source data samples at all.