RDumb++: Drift-Aware Continual Test-Time Adaptation

TL;DR

RDumb++ enhances continual test-time adaptation by incorporating drift detection and adaptive resets, significantly improving long-term accuracy on evolving data streams.

Contribution

It introduces entropy-based and KL-divergence drift detection mechanisms with reset strategies, enabling more reliable long-horizon adaptation in test-time scenarios.

Findings

Achieves approximately 3% accuracy improvement over RDumb.

Maintains stable adaptation over long data streams.

Drift-aware resetting prevents prediction collapse.

Abstract

Continual Test-Time Adaptation (CTTA) seeks to update a pretrained model during deployment using only the incoming, unlabeled data stream. Although prior approaches such as Tent, EATA etc. provide meaningful improvements under short evolving shifts, they struggle when the test distribution changes rapidly or over extremely long horizons. This challenge is exemplified by the CCC benchmark, where models operate over streams of 7.5M samples with continually changing corruption types and severities. We propose RDumb++, a principled extension of RDumb that introduces two drift-detection mechanisms i.e entropy-based drift scoring and KL-divergence drift scoring, together with adaptive reset strategies. These mechanisms allow the model to detect when accumulated adaptation becomes harmful and to recover before prediction collapse occurs. Across CCC-medium with three speeds and three seeds (nine runs, each containing one million samples), RDumb++ consistently surpasses RDumb, yielding approx 3% absolute accuracy gains while maintaining stable adaptation throughout the entire stream. Ablation experiments on drift thresholds and reset strengths further show that drift-aware resetting is essential for preventing collapse and achieving reliable long-horizon CTTA.