lucidrains / lucidrains/vector-quantize-pytorch
EMA update on CosineCodebook
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- Dominant language
- Python
- Stars
- 4k
- Forks
- 338
- PR merge metrics
- No merged PRs in 30d
Description
The original VIT-VQGAN paper does not seem to use EMA update for codebook learning since their codebook is unit-normalized vectors.
Particularly, to my understanding, EMA update does not quite make sense when the encoder outputs and codebook vectors are unit-normalized ones.
What's your take on this? Should we NOT use EMA update with CosineCodebook?
Contributor guide
No contributing guide indexed for this repository
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 at the CosineCodebook entry point and trace how EMA update relates to the unit-normalized encoder outputs and codebook vectors. Compare that behavior with the original VIT-VQGAN paper, then determine whether the project should retain or remove EMA for this codebook. The issue names no file or test, so completion would require an agreed design and corresponding validation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100