Summary of Wavelets Are All You Need For Autoregressive Image Generation, by Wael Mattar et al.
Wavelets Are All You Need for Autoregressive Image Generation
by Wael Mattar, Idan Levy, Nir Sharon, Shai Dekel
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 presents a novel approach to autoregressive image generation that leverages two key components: wavelet image coding and a variant of language transformer optimized for token sequences. The wavelet coding framework allows for the tokenization of visual details from coarse to fine, while the re-designed language transformer learns statistical correlations within these token sequences, which are reflective of well-known correlations between wavelet subbands at various resolutions. Experimental results demonstrate the effectiveness of this approach in generating images conditioned on the generation process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates new ways for computers to generate images that look like real pictures. It uses two main ideas: a way to break down an image into smaller parts based on how important they are, and a special kind of language model that can understand these small parts. The model learns patterns within the broken-down image details, which is similar to how we see patterns in different parts of an image. By combining these ideas, the paper shows that it’s possible to generate images that look realistic and follow specific rules. |
Keywords
» Artificial intelligence » Autoregressive » Image generation » Language model » Token » Tokenization » Transformer
