arXiv Computer Vision
When Test-Time Adaptation Helps, Harms, or Becomes Inactive: A Condition-Level Study on CIFAR-10-C
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arXiv Machine Learning
Jun 2
A Closer Look at In-Distribution vs. Out-of-Distribution Accuracy for Open-Set Test-time Adaptation
arXiv:2606. 01973v1 Announce Type: new Abstract: Open-set test-time adaptation (TTA) updates models on new data in the presence of input shifts and unknown output classes.
arXiv AI
2d ago
Adapting Without Gradients: Affine Statistics Transport and What Its Certificate Can Tell You
The paper introduces CASTER, a gradient‑free test‑time adaptation method that keeps the model frozen by storing source class statistics in a discriminative subspace and applying an affine transformation estimated from target‑batch moments. CASTER avoids backward passes, optimizer state, and large feature banks, outperforming k‑NN on frozen features in most backbone‑dataset settings while using far less memory. The authors also propose a residual‑to‑margin transportability certificate that flags when affine transport is unreliable, and demonstrate that gating based on this certificate can recover performance losses.
arXiv Machine Learning
3d ago
Continual Test-Time Adaptation via Entropy Sensitivity-Guidance in Strict Online Setting
arXiv:2608.29920v1 Announce Type: cross Abstract: Test-time adaptation (TTA) promises robustness under distribution shift by updating a pretrained model on unlabeled test data, but strict online TTA...
arXiv Machine Learning
Aug 4
Logit-Origin Centering for Singleton Test-Time Adaptation
arXiv:2608. 01074v1 Announce Type: new Abstract: Tabular data is used extensively in many real-world use cases.
arXiv AI
Jun 2
DataShield: Safety-degrading Data Filtering for LLM Benign Instruction Fine-Tuning
arXiv:2606. 00160v1 Announce Type: cross Abstract: Large language models (LLMs) suffer from degraded safety capabilities even when fine-tuned with benign datasets.
Hugging Face Trending Papers
Jun 21
Reliability-Guided Adaptive Ensembling for Robust Test-Time Adaptation
Test-time adaptation (TTA) can mitigate domain shift without source data, but it is highly brittle under adversarially contaminated test streams, where corrupted inputs also destabilize online updates. We study robust test-time adaptation (RTTA) in the adversarial-stream setting, which remains comparatively underexplored relative to standard TTA, and propose SAFER (Stochastic Augmentation Framework for Enhanced Robustness), a training-free reliability-guided augmentation wrapper for RTTA.