Reliability-Gated Source Anchoring for Continual Test-Time Adaptation
About
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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Image Classification | CINIC-10 iid (test) | Test Accuracy50.51 | 34 | |
| Continual Test-Time Adaptation | CCC Easy | Error (%)35.97 | 32 | |
| Continual Test-Time Adaptation | CCC Medium | Error (%)41.66 | 32 | |
| Continual Test-Time Adaptation | CCC Hard | Error (%)76.36 | 32 | |
| Continual Test-Time Adaptation | CIN-C iid | Error (%)49.49 | 16 | |
| Continual Test-Time Adaptation | CIN-C (corr split) | Error (%)49.92 | 16 | |
| Image Classification | CCC Easy | Accuracy64.03 | 16 | |
| Image Classification | CCC Medium | Accuracy58.34 | 16 | |
| Image Classification | CCC Hard | Accuracy23.64 | 16 | |
| Continual Test-Time Adaptation | ImageNet-C | Error Rate57.66 | 8 |