lucidrains / lucidrains/vector-quantize-pytorch
Extracting learnt embeddings for analysis
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- Python
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Description
Hi @lucidrains,
Amazing work on this package, it is really helpful!
I am using my the QINCo (ResVQ + implicit_neural_codebook = True) for my work. I have trained my model on the data with 8 quantizers. I would now like to assess the quality of my embeddings by linear probing. I am a little confused with what to use as my embeddings for the same. I could either use `raw_embeddings=res_vq_layer.codebook` or `embeddings=residual_vq_layer.get_codes_from_indices(indices)` (as per issue #44). Could you guide me with the same?
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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 with issue #44 and inspect the two mentioned entry points: res_vq_layer.codebook and residual_vq_layer.get_codes_from_indices(indices). Determine which representation is intended for linear probing in the QINCo ResVQ configuration, then document that guidance and any relevant usage details.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100