Distribution Alignment for Fully Test-Time Adaptation with Dynamic Online Data Streams

TL;DR

This paper introduces a novel distribution alignment loss for test-time adaptation that effectively handles non-i.i.d. data streams with domain shifts by aligning test features with source distributions, improving robustness and performance.

Contribution

The paper proposes a distribution alignment loss and a domain shift detection mechanism to enhance test-time adaptation in non-i.i.d. data streams, addressing conflicts in optimization objectives.

Findings

Outperforms existing TTA methods in non-i.i.d. scenarios.

Maintains competitive performance under ideal i.i.d. conditions.

Effective in continual domain shift scenarios.

Abstract

Given a model trained on source data, Test-Time Adaptation (TTA) enables adaptation and inference in test data streams with domain shifts from the source. Current methods predominantly optimize the model for each incoming test data batch using self-training loss. While these methods yield commendable results in ideal test data streams, where batches are independently and identically sampled from the target distribution, they falter under more practical test data streams that are not independent and identically distributed (non-i.i.d.). The data batches in a non-i.i.d. stream display prominent label shifts relative to each other. It leads to conflicting optimization objectives among batches during the TTA process. Given the inherent risks of adapting the source model to unpredictable test-time distributions, we reverse the adaptation process and propose a novel Distribution Alignment loss for TTA. This loss guides the distributions of test-time features back towards the source distributions, which ensures compatibility with the well-trained source model and eliminates the pitfalls associated with conflicting optimization objectives. Moreover, we devise a domain shift detection mechanism to extend the success of our proposed TTA method in the continual domain shift scenarios. Our extensive experiments validate the logic and efficacy of our method. On six benchmark datasets, we surpass existing methods in non-i.i.d. scenarios and maintain competitive performance under the ideal i.i.d. assumption.