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Test-Time Optimization Turns Frozen Reconstructors into Imputers for Missing Time-Series Channels

This paper introduces a test-time optimization method that converts a frozen time-series reconstructor into an imputer for complete channel failures, without mask augmentation during training. Missing entries are optimized to minimize reconstruction error on observed coordinates, with an optional temporal-smoothness prior. Across five datasets and four architectures, the method reduces MAE in 19 of 20 combinations versus one-pass reconstruction, though it trails mask-aware imputers on three datasets and requires more inference time.

arxiv14h agoScore 78

time-series imputationtest-time adaptationmissing data handling

Research Editor selected

Uncertainty-Aware Prototype Learning for Open-Set Test-Time Adaptation

This paper introduces UAPA, a plug-and-play method for open-set test-time adaptation that handles both covariate-shifted in-distribution and out-of-distribution samples. It combines uncertainty-driven ID/OOD separation, dynamic prototype refinement, and contrastive prototype entropy adaptation to improve adaptation reliability. Experiments on CIFAR benchmarks and Tiny-ImageNet-C show average OSCR improvements of 2.08 and 1.28 percentage points over strong baselines.

Why it matters: Presents a novel, concrete method with empirical gains for a challenging open-set TTA problem, useful for researchers and practitioners.

arxiv1d agoScore 82

Test-Time AdaptationOpen-Set RecognitionPrototype Learning