Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Paper: Data-Distortion Guided Self-Distillation for Deep Neural Networks #127

Open
yiqings opened this issue Mar 29, 2022 · 2 comments
Open

Comments

@yiqings
Copy link

yiqings commented Mar 29, 2022

Description

1. A self distillation scheme built upon distilling different augmented/distorted images by the same student. 
2.A MMD loss distilling the features between different augmented/distorted images

Modifications

Probably removing the MMD loss and only retain the KL loss is fine,
since it can already demonstrate competitive performance.

The methods shows to be a very powerful self-distillation scheme, even with the absence of MMD loss, with my my local experiments on CIFAR10/100.

Plus, it also demonstrate a strong compatibility with other distillation scheme, and can perform as a component.

@yiqings
Copy link
Author

yiqings commented Mar 29, 2022

https://github.com/youngerous/ddgsd-pytorch provides an unofficial implementation.

@NeelayS
Copy link
Member

NeelayS commented Mar 30, 2022

Hi @yiqings, thanks for raising this issue. Unfortunately, development for KD-Lib has stalled for now, but we will be sure to keep this issue in mind when / if we resume.
Also, do let me know if you would be interested in contributing an implementation for this paper.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants