StableTTA improves ImageNet-1K accuracy across 71 vision models by stabilizing logit aggregation under coherent-batch inference and enabling efficient single-forward-pass adaptation.
Better aggregation in test-time augmentation
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
One-class methods DSVDD and DROC outperform MIL baselines for instance-level detection of rare malignant cells at witness rates ≤1% on bone marrow and oral cytology datasets.
citing papers explorer
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StableTTA: Improving Vision Model Performance by Training-free Test-Time Adaptation Methods
StableTTA improves ImageNet-1K accuracy across 71 vision models by stabilizing logit aggregation under coherent-batch inference and enabling efficient single-forward-pass adaptation.
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Needle in a Haystack: One-Class Representation Learning for Detecting Rare Malignant Cells in Computational Cytology
One-class methods DSVDD and DROC outperform MIL baselines for instance-level detection of rare malignant cells at witness rates ≤1% on bone marrow and oral cytology datasets.