Sample-wise neural collapse reveals that feature-classifier misalignment drives TTA degradation under shifts, which NCTTA corrects via hybrid geometric-predictive targets.
Entropy is not enough for test-time adaptation: From the perspective of disentangled factors
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
representative citing papers
CoDiRe blends VLM and target model predictions via MSP-based weighting and Optimal Transport rectification to enable stable continual test-time adaptation, outperforming CoTTA by 10.55% on ImageNet-C at 48% of the compute cost.
A multi-level diversification wrapper for test-time adaptation that treats entropy minimization as multi-hypothesis inference to reduce underspecification and improve robustness by 1-4%.
DOME learns sample-specific domain variables from sparse supervision via vision-language models and a sparse domain bank to improve test-time adaptation performance.
Casting Tent, EATA, SAR, DeYO, and COME into DP-TTA via per-sample clipping and Gaussian noise yields adequate privacy on ImageNet-C at modest accuracy and compute cost, with clipping sometimes improving stability.
Proposes meta-learning attack with priority-aware gradient alignment for sample-wise targeted attacks on TTA that maintain label distribution consistency with no-attack baseline.
citing papers explorer
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Neural Collapse in Test-Time Adaptation
Sample-wise neural collapse reveals that feature-classifier misalignment drives TTA degradation under shifts, which NCTTA corrects via hybrid geometric-predictive targets.
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Test-Time Distillation for Continual Model Adaptation
CoDiRe blends VLM and target model predictions via MSP-based weighting and Optimal Transport rectification to enable stable continual test-time adaptation, outperforming CoTTA by 10.55% on ImageNet-C at 48% of the compute cost.
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Multi-Hypothesis Test-Time Adaptation to Mitigate Underspecification
A multi-level diversification wrapper for test-time adaptation that treats entropy minimization as multi-hypothesis inference to reduce underspecification and improve robustness by 1-4%.
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DOME: Learning Transferable Domain Variables from Sparse Supervision for Test-Time Adaptation
DOME learns sample-specific domain variables from sparse supervision via vision-language models and a sparse domain bank to improve test-time adaptation performance.
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Private and Stable Test-Time Adaptation with Differential Privacy
Casting Tent, EATA, SAR, DeYO, and COME into DP-TTA via per-sample clipping and Gaussian noise yields adequate privacy on ImageNet-C at modest accuracy and compute cost, with clipping sometimes improving stability.
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Sample-wise Targeted Adversarial Attacks on Test-time Adaptation
Proposes meta-learning attack with priority-aware gradient alignment for sample-wise targeted attacks on TTA that maintain label distribution consistency with no-attack baseline.