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About this paper
Yinbo Chen, Rohit Girdhar, Xiaolong Wang, Sai Saketh Rambhatla, Ishan Misra
UC San Diego and Meta researchers introduce DiTo (Diffusion Tokenizer), a breakthrough self-supervised approach to image tokenization that achieves superior reconstruction quality through a simplified single-loss training framework, eliminating the need for complex loss combinations while maintaining theoretical rigor through diffusion model principles.
Key insights
- Complex tokenizer training can be effectively simplified to a single loss function
- Self-supervised learning can match or exceed supervised approaches in this domain
- Noise synchronization improves structured visual element handling (text, symbols)
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.