Variational_Information_Distillation
Reproducing VID in CVPR2019 (on working)
Stars
17
Ecosystems:
Python
Variational_Information_DIstillation
Project of Reproducing "VID" involved in https://github.com/rp12-study/rp12-hub
Abstract
- Pros
- Cons
Requirements
- python==3.x
- tensorflow>=1.13.0
- Scipy
How to run
Note that
-
I found the author's code at https://github.com/ssahn0215/variational-information-distillation. However I'll not refer it, cause I want to check reproducibility of the paper.I don't know why but the author deleted his repository. - My experimental results are higher than the paper. I found that It is tough to make such a low performance like paper. For this, I removed gamma and regularization of batch normalization, and modify hyper-parameters to make training unstable.
- The authors said "We choose four pairs of intermediate layers similarly to [31], each of which is located at the end of a group of residual blocks." but there are only three groups of residual blocks in WResNet. So I sense one more feature map after the first convolutional layer.
- I'll not follow the author's configuration for comparative methods. Because their modification look somewhat awkward, unfair and not coinside with the proposed ways. Also, I think that for fair comparison should not modify the original author configutation whether good or not. It means that I'll only reprocude the author's method, VID.
Experiment results
TO DO
- Check correctness of VID implementation and do experiments
- edit README