AETTA: Label-Free Accuracy Estimation for Test-Time Adaptation
Taeckyung Lee Sorn Chottananurak Taesik Gong Sung-Ju Lee
Test-time adaptation (TTA) has emerged as a viable solution to adapt
pre-trained models to domain shifts using unlabeled test data. However, TTA
faces challenges of adaptation failures due to its reliance on blind adaptation
to unknown test samples in dynamic scenarios. Traditional methods for
out-of-distribution performance estimation are limited by unrealistic
assumptions in the TTA context, such as requiring labeled data or re-training
models. To address this issue, we propose AETTA, a label-free accuracy
estimation algorithm for TTA. We propose the prediction disagreement as the
accuracy estimate, calculated by comparing the target model prediction with
dropout inferences. We then improve the prediction disagreement to extend the
applicability of AETTA under adaptation failures. Our extensive evaluation with
four baselines and six TTA methods demonstrates that AETTA shows an average of
19.8%p more accurate estimation compared with the baselines. We further
demonstrate the effectiveness of accuracy estimation with a model recovery case
study, showcasing the practicality of our model recovery based on accuracy
estimation. The source code is available at https://github.com/taeckyung/AETTA.