Summary of When Worse Is Better: Navigating the Compression-generation Tradeoff in Visual Tokenization, by Vivek Ramanujan et al.
When Worse is Better: Navigating the compression-generation tradeoff in visual tokenization
by Vivek Ramanujan, Kushal Tirumala, Armen Aghajanyan, Luke Zettlemoyer, Ali Farhadi
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 A novel approach to image generation is proposed, which challenges the conventional two-stage training method used in latent diffusion and discrete token-based generation models. The study reveals that better reconstruction performance does not always lead to better generation, as smaller generative models can benefit from more compressed latents despite worse reconstruction. To optimize this trade-off, the authors introduce Causally Regularized Tokenization (CRT), which embeds useful biases in stage 1 latents based on knowledge of the stage 2 generation procedure. This regularization improves compute efficiency by 2-3 times over baseline and matches state-of-the-art discrete autoregressive ImageNet generation with reduced tokens per image and model parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to create a picture from scratch, but instead of drawing it yourself, you’re using a special computer program that helps you make the right choices. This program is trained on lots of pictures and learns how to break them down into smaller pieces called “tokens.” The problem is that this training process can be very slow and uses a lot of computer power. Scientists have been trying to find ways to make this process faster and more efficient, but it’s been tricky. In this paper, the authors propose a new method called Causally Regularized Tokenization (CRT) that helps make the process faster and better by giving the program a hint about what kind of picture it should be making. |
Keywords
» Artificial intelligence » Autoregressive » Diffusion » Image generation » Regularization » Token » Tokenization
