  The majority of existing Unsupervised Domain Adaptation (UDA) methods
presumes 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 always considered to be available, even though performing
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 a unified method to tackle adaptive noise filtering and
pseudo-label refinement. A novel Negative Ensemble Learning technique is
devised to specifically address noise in pseudo-labels, by enhancing diversity
in ensemble members with different stochastic (i) input augmentation and (ii)
feedback. In particular, the latter is achieved by leveraging the novel concept
of Disjoint Residual Labels, which allow diverse information to be fed to the
different members. A single target model is eventually trained with the refined
pseudo-labels, which leads to a robust performance on the target domain.
Extensive experiments show that the proposed method, named Adaptive
Pseudo-Label Refinement, achieves state-of-the-art performance on major UDA
benchmarks, such as Digit5, PACS, Visda-C, and DomainNet, without using source
data at all.
