Summary of Representation Collapsing Problems in Vector Quantization, by Wenhao Zhao et al.
Representation Collapsing Problems in Vector Quantization
by Wenhao Zhao, Qiran Zou, Rushi Shah, Dianbo Liu
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the phenomenon of “representation collapse” in vector quantization, a technique used to discretize continuous representations in machine learning models. Specifically, it focuses on how codebook tokens or latent embeddings lose their discriminative power and converge to a limited subset of values, compromising the model’s ability to capture diverse data patterns. The study uses both synthetic and real datasets to identify the severity of different types of collapses and their triggering conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Vector quantization is used in large language models, diffusion models, and other generative models to tokenize data representations. However, it remains unclear how vector quantization behaves in these models. This paper explores this issue by looking at “representation collapse” – when codebook tokens or latent embeddings lose their ability to capture different patterns in the data. |
Keywords
» Artificial intelligence » Machine learning » Quantization
