The Over-Certainty Phenomenon in Modern Test-Time Adaptation Algorithms

• URL: http://arxiv.org/abs/2404.16168v4
• Date: Sun, 07 Sep 2025 02:54:23 GMT
• Title: The Over-Certainty Phenomenon in Modern Test-Time Adaptation Algorithms
• Authors: Fin Amin, Jung-Eun Kim,
• Abstract summary: We propose a solution that maintains accuracy and addresses calibration.<n>Our method achieves state-of-the-art performance in terms of Expected Error and Negative Log Likelihood.
• Score: 8.210473195536077
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: When neural networks are confronted with unfamiliar data that deviate from their training set, this signifies a domain shift. While these networks output predictions on their inputs, they typically fail to account for their level of familiarity with these novel observations. Prevailing works navigate test-time adaptation with the goal of curtailing model entropy, yet they unintentionally produce models that struggle with sub-optimal calibration-a dilemma we term the over-certainty phenomenon. This over-certainty in predictions can be particularly dangerous in the setting of domain shifts, as it may lead to misplaced trust. In this paper, we propose a solution that not only maintains accuracy but also addresses calibration by mitigating the over-certainty phenomenon. To do this, we introduce a certainty regularizer that dynamically adjusts pseudo-label confidence by accounting for both backbone entropy and logit norm. Our method achieves state-of-the-art performance in terms of Expected Calibration Error and Negative Log Likelihood, all while maintaining parity in accuracy.

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