Summary of Rate-adaptive Quantization: a Multi-rate Codebook Adaptation For Vector Quantization-based Generative Models, by Jiwan Seo et al.
Rate-Adaptive Quantization: A Multi-Rate Codebook Adaptation for Vector Quantization-based Generative Models
by Jiwan Seo, Joonhyuk Kang
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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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 introduces Rate-Adaptive Quantization (RAQ), a novel framework for vector quantization (VQ)-based generative models that enables flexible tradeoffs between compression and reconstruction fidelity. RAQ applies a data-driven approach to generate variable-rate codebooks from a single baseline VQ model, allowing for efficient adaptation to different bitrate requirements. The authors also propose a simple clustering-based procedure for pre-trained VQ models, offering an alternative when retraining is infeasible. Experiments show that RAQ performs effectively across multiple rates, often outperforming conventional fixed-rate VQ baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us learn more efficient ways to store and recreate pictures or sounds using computer models. It shows how to make these models work better by changing the way they compress information. The authors created a new system that can do this easily, without needing to retrain the model from scratch. This makes it useful for many different tasks, like storing images or music. |
Keywords
» Artificial intelligence » Clustering » Quantization
