Towards Open-Set Test-Time Adaptation Utilizing the Wisdom of Crowds in Entropy Minimization

TL;DR

This paper introduces a sample selection method for test-time adaptation that leverages the 'wisdom of crowds' phenomenon, filtering out noisy samples to improve long-term adaptation stability in image classification and segmentation.

Contribution

It proposes a novel confidence-based filtering technique inspired by empirical findings on entropy minimization, enhancing existing TTA methods' performance.

Findings

Significant error reduction in image classification (49.4%)

Improved segmentation accuracy (11.7% mIoU gain)

Applicable to various TTA methods

Abstract

Test-time adaptation (TTA) methods, which generally rely on the model's predictions (e.g., entropy minimization) to adapt the source pretrained model to the unlabeled target domain, suffer from noisy signals originating from 1) incorrect or 2) open-set predictions. Long-term stable adaptation is hampered by such noisy signals, so training models without such error accumulation is crucial for practical TTA. To address these issues, including open-set TTA, we propose a simple yet effective sample selection method inspired by the following crucial empirical finding. While entropy minimization compels the model to increase the probability of its predicted label (i.e., confidence values), we found that noisy samples rather show decreased confidence values. To be more specific, entropy minimization attempts to raise the confidence values of an individual sample's prediction, but individual confidence values may rise or fall due to the influence of signals from numerous other predictions (i.e., wisdom of crowds). Due to this fact, noisy signals misaligned with such 'wisdom of crowds', generally found in the correct signals, fail to raise the individual confidence values of wrong samples, despite attempts to increase them. Based on such findings, we filter out the samples whose confidence values are lower in the adapted model than in the original model, as they are likely to be noisy. Our method is widely applicable to existing TTA methods and improves their long-term adaptation performance in both image classification (e.g., 49.4% reduced error rates with TENT) and semantic segmentation (e.g., 11.7% gain in mIoU with TENT).