LEAP is an adaptive layer-skipping curriculum for ViT feature distillation that reports accuracy gains on ImageNet and retrieval tasks plus training compute savings.
Curriculum temperature for knowledge distillation.arXiv preprint arXiv:2211.16231, 2022
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Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.
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LEAP: Layer-skipping Efficiency via Adaptive Progression for Vision Transformer Distillation
LEAP is an adaptive layer-skipping curriculum for ViT feature distillation that reports accuracy gains on ImageNet and retrieval tasks plus training compute savings.
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APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.