pre-activation ResNet
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- Python
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Description
Is there any interest in implementing the "pre-activation" version of ResNet, as described in
https://arxiv.org/pdf/1603.05027.pdf
and which has been show to have better performance on CIFAR 10/100 and Imagenet? It's a relatively minor modification of the ResNet version that's already implemented -- just changing the order of some of the operations in the residual block.
I'm happy to implement and submit a PR, though I don't really have the resources to train an Imagenet model ATM.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start from the existing ResNet implementation and compare its residual block with the pre-activation design described in the linked paper. Apply the operation-order change for the existing CIFAR-10/100 and ImageNet model variants, then verify the model behavior with the repository's available checks; ImageNet training is not required by the issue.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100