Summary of Addressing Representation Collapse in Vector Quantized Models with One Linear Layer, by Yongxin Zhu et al.
Addressing Representation Collapse in Vector Quantized Models with One Linear Layer
by Yongxin Zhu, Bocheng Li, Yifei Xin, Linli Xu
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 proposes a novel method, SimVQ, to mitigate representation collapse in Vector Quantization (VQ) models. VQ is widely used for converting continuous representations into discrete codes, but existing methods that address representation collapse often reduce the dimensionality of latent space at the expense of model capacity. The authors identify the primary cause of representation collapse as the disjoint optimization of the codebook and develop a linear transformation layer-based approach to optimize the entire linear space spanned by the codebook. This approach resolves the collapse issue in VQ models with just one linear layer. The paper validates the efficacy of SimVQ through extensive experiments across various modalities, including image and audio data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem that makes Vector Quantization (VQ) less useful. When we use VQ to make computers understand things, some parts don’t work well because they get stuck in one place. The authors found out why this happens and created a new way to fix it. They did this by changing the way the computer looks at the information. Instead of just looking at small bits, the new method looks at all the bits together. This makes it better at understanding things. The paper shows that this new method works well with different kinds of data, like pictures and sounds. |
Keywords
» Artificial intelligence » Latent space » Optimization » Quantization
