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

TMLR Paper4661 Authors

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.

Submission Length: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission:

This paper was not submitted to TMLR previously. This is a revision we are uploading during the rebuttal. The changes with respect to the initial submission will be outlined in an official comment.

Fixed a typo.

Assigned Action Editor: ~Eleni_Triantafillou1

Submission Number: 4661