EGAD adaptively distills LLM knowledge at the token level by using entropy to create a curriculum from low- to high-entropy tokens, adjust temperature, and switch between logits-only and feature-based branches.
arXiv preprint arXiv:2502.00631 (2025) 18 Authors Suppressed Due to Excessive Length
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SegTTA improves MedSAM2 zero-shot segmentation on uterus and liver datasets by test-time augmentations plus weighted voting, delivering +1.6 mIoU and -2.0 HD95 on multiclass hepatic vessels.
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EGAD: Entropy-Guided Adaptive Distillation for Token-Level Knowledge Transfer
EGAD adaptively distills LLM knowledge at the token level by using entropy to create a curriculum from low- to high-entropy tokens, adjust temperature, and switch between logits-only and feature-based branches.
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SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation
SegTTA improves MedSAM2 zero-shot segmentation on uterus and liver datasets by test-time augmentations plus weighted voting, delivering +1.6 mIoU and -2.0 HD95 on multiclass hepatic vessels.