Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
Rethinking soft labels for knowledge distillation: A bias-variance tradeoff perspective
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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SAAD adaptively weights adversarial training samples by their transferability to the teacher, yielding higher AutoAttack robustness than prior distillation methods on CIFAR and Tiny-ImageNet without extra compute.
Leakage-aware distillation transfers at least 90% of tabular foundation model AUC to lightweight students across 19 health datasets, with 26x CPU speedup and preserved calibration/fairness.
Audits miscalibration in LLM-based social science measurements across 14 constructs and proposes a soft label distillation pipeline that reduces ECE by 43.2% and Brier score by 34.0% on average.
citing papers explorer
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Toward Calibrated, Fair, and accurate Deepfake Detection
Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
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Distilling Tabular Foundation Models for Structured Health Data
Leakage-aware distillation transfers at least 90% of tabular foundation model AUC to lightweight students across 19 health datasets, with 26x CPU speedup and preserved calibration/fairness.
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Assessing and Mitigating Miscalibration in LLM-Based Social Science Measurement
Audits miscalibration in LLM-based social science measurements across 14 constructs and proposes a soft label distillation pipeline that reduces ECE by 43.2% and Brier score by 34.0% on average.