Summary of Stabilize the Latent Space For Image Autoregressive Modeling: a Unified Perspective, by Yongxin Zhu et al.
Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
by Yongxin Zhu, Bocheng Li, Hang Zhang, Xin Li, Linli Xu, Lidong Bing
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 explores the optimal choice of generative models for image generation tasks. Latent-based image generative models like LDMs and MIMs have achieved success, but it’s unclear if they’re truly the best option. The authors investigate this by comparing autoregressive models with these latent-based models and find that despite sharing a common latent space, autoregressive models lag behind in image generation. To address this discrepancy, the paper introduces a unified perspective on the relationship between latent space and generative models, emphasizing the stability of latent space in image generative modeling. The authors also propose a discrete image tokenizer (DiGIT) to stabilize the latent space for image generative modeling. Experimental results show that DiGIT benefits both image understanding and generation with the next token prediction principle. This is the first time an autoregressive model has outperformed LDMs, highlighting the potential of optimized latent spaces and integrated discrete tokenization in advancing image generative models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to make computers create realistic images. There are different ways to do this, like using a model that looks at what it means for an image to look good (latent-based). But it’s not clear if these models are the best option. The researchers looked into this by comparing different kinds of models and found that one type of model didn’t do as well even though they shared the same “code” or blueprint. They then came up with a new way to think about how these models work together, which helped them make better images. They also created a special tool (DiGIT) to help these models be more stable and create better pictures. |
Keywords
» Artificial intelligence » Autoregressive » Image generation » Latent space » Token » Tokenization » Tokenizer
