Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
When does label smoothing help?arXiv preprint arXiv:1906.02629
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RefCal uses a supervised contrastive loss to promote refinement alongside calibration in DNN training, reporting better accuracy, refinement, and ECE than baselines on imbalanced CIFAR-100-LT.
Sparse-to-dense 3D segmentation from 2D slices shows divergent regularization needs: 2D benefits from strong augmentation and soft labels while 3D does not, and human-centric preprocessing harms performance.
LiLAW learns to weight samples as easy, moderate or hard using three global scalars updated by one gradient step on a validation batch to improve noisy training performance.
Develops a margin-adaptive learned confidence estimator for LLMs with generalization guarantees to improve agreement rates with human judgments over heuristic baselines.
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LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training
LiLAW learns to weight samples as easy, moderate or hard using three global scalars updated by one gradient step on a validation batch to improve noisy training performance.