Guiding Pseudo-labels with Uncertainty Estimation for Test-Time Adaptation

• URL: http://arxiv.org/abs/2303.03770v1
• Date: Tue, 7 Mar 2023 10:04:55 GMT
• Title: Guiding Pseudo-labels with Uncertainty Estimation for Test-Time Adaptation
• Authors: Mattia Litrico, Alessio Del Bue, Pietro Morerio
• Abstract summary: Test-Time Adaptation (TTA) is a specific case of Unsupervised Domain Adaptation (UDA) where a model is adapted to a target domain without access to source data. We propose a novel approach for the TTA setting based on a loss reweighting strategy that brings robustness against the noise that inevitably affects the pseudo-labels.
• Score: 27.233704767025174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Standard Unsupervised Domain Adaptation (UDA) methods assume the availability of both source and target data during the adaptation. In this work, we investigate the Test-Time Adaptation (TTA), a specific case of UDA where a model is adapted to a target domain without access to source data. We propose a novel approach for the TTA setting based on a loss reweighting strategy that brings robustness against the noise that inevitably affects the pseudo-labels. The classification loss is reweighted based on the reliability of the pseudo-labels that is measured by estimating their uncertainty. Guided by such reweighting strategy, the pseudo-labels are progressively refined by aggregating knowledge from neighbouring samples. Furthermore, a self-supervised contrastive framework is leveraged as a target space regulariser to enhance such knowledge aggregation. A novel negative pairs exclusion strategy is proposed to identify and exclude negative pairs made of samples sharing the same class, even in presence of some noise in the pseudo-labels. Our method outperforms previous methods on three major benchmarks by a large margin. We set the new TTA state-of-the-art on VisDA-C and DomainNet with a performance gain of +1.8\% on both benchmarks and on PACS with +12.3\% in the single-source setting and +6.6\% in\ multi-target adaptation. Additional analyses demonstrate that the proposed approach is robust to the noise, which results in significantly more accurate pseudo-labels compared to state-of-the-art approaches.

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