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Reliability-Gated Source Anchoring for Continual Test-Time Adaptation

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Continual test-time adaptation (CTTA) updates a pretrained model online on an unlabeled, non-stationary stream while anchoring it to a frozen source checkpoint. This anchor is useful only when the source remains reliable. On CCC-Hard, however, a ResNet-50 source falls to approximately $1.3\%$ top-$1$ accuracy, while existing source-anchored CTTA methods continue applying the same anchor strength. We call this failure mode blind anchoring and propose RMemSafe, a reliability-gated extension of ROID that uses the frozen source's normalized predictive entropy to attenuate all explicit source-coupled uses in the objective. When the source posterior approaches uniformity, the gate closes: the source anchor and agreement filter vanish, and the objective reduces to a source-agnostic fallback comprising ROID's base losses plus marginal calibration. Combined with ASR, RMemSafe achieves the lowest error on $8$ of $9$ matched-split continual-corruption cells and is the best reset-based method on all $9$, improving ROID+ASR by $1.05$~pp on ResNet-50 and $0.48$~pp on ViT-B/16. A controlled source-degradation sweep shows a $1.13{\times}$ shallower harm slope than ROID+ASR, consistent with the graceful-decay prediction. The entropy gate detects high-entropy source collapse, not confidently wrong low-entropy sources; this scope is explicitly evaluated and discussed.

Vikash Singh, Debargha Ganguly, Weicong Chen, Sabyasachi Sahoo, Sreehari Sankar, Biyao Zhang, Mohsen Hariri, Shouren Wang, Osama Zafar, Christian Gagn\'e, Vipin Chaudhary• 2026

Related benchmarks

TaskDatasetResultRank
Image ClassificationCINIC-10 iid (test)
Test Accuracy50.51
34
Continual Test-Time AdaptationCCC Easy
Error (%)35.97
32
Continual Test-Time AdaptationCCC Medium
Error (%)41.66
32
Continual Test-Time AdaptationCCC Hard
Error (%)76.36
32
Continual Test-Time AdaptationCIN-C iid
Error (%)49.49
16
Continual Test-Time AdaptationCIN-C (corr split)
Error (%)49.92
16
Image ClassificationCCC Easy
Accuracy64.03
16
Image ClassificationCCC Medium
Accuracy58.34
16
Image ClassificationCCC Hard
Accuracy23.64
16
Continual Test-Time AdaptationImageNet-C
Error Rate57.66
8
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