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License: Apache License 2.0
Awesome Knowledge Distillation
License: Apache License 2.0
Knowledge Adaptation for Efficient Semantic Segmentation
https://arxiv.org/abs/1903.04688
Structured Knowledge Distillation for Semantic Segmentation
https://arxiv.org/abs/1903.04197
Some papers are far away from "awesome". For example, the paper <<Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification>> was rejected by ICLR2018. Maybe the paper is still unpublished.
I hope that the owner of this repo could be more strict with the quality of the listed papers, and append more information about the publication status of the papers. If some paper got rejected, it's likely that it's not an "awesome" paper
Hi,
My name is Marco Mistretta, and I am a researcher at MICC (Florence, Italy).
Thank you for creating this repository! It has been an invaluable resource for me.
Could you please add our ECCV24 paper, Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation?
It's the first time that unsupervised knowledge distillation has been applied to prompt learning in the few-shot setting!
Thank you for your time and consideration!
TITLE: Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation
CONFERENCE: ECCV24
PAPER: https://arxiv.org/abs/2407.03056
CODE: https://github.com/miccunifi/KDPL
Thanks for creating this repository and gathering lots of useful papers and materials. I think you can add the following paper to this list:
Also, there is a good implementation of this paper in PyTorch.
Hi @dkozlov
I notice that there is no paper in 2024 listed yet. Could you please add a relevant paper from CVPR 2024 Highlight. The information is given below:
Title: Logit Standardization in Knowledge Distillation
Paper: https://arxiv.org/abs/2403.01427
Github: https://github.com/sunshangquan/logit-standardization-KD
Supplements: https://sunsean21.github.io/resources/cvpr2024_supp.pdf
The paper discusses the possibility of assigning temperatures distinctly between teacher/student and dynamically across samples. It then proposes a weighted Z-score logit standardization as a plug-and-play preprocess, capable of boosting the existing logit-based KD methods.
Thank you for your attention.
A large scale Awesome KD repository containing more papers:
https://github.com/FLHonker/Awesome-Knowledge-Distillation
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