Summary of Structure-guided Adversarial Training Of Diffusion Models, by Ling Yang et al.
Structure-Guided Adversarial Training of Diffusion Models
by Ling Yang, Haotian Qian, Zhilong Zhang, Jingwei Liu, Bin Cui
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces Structure-guided Adversarial training of Diffusion Models (SADM), a novel approach that enhances the performance of diffusion models in generative applications. By incorporating structure-guided adversarial training, SADM learns to capture pair-wise relationships among samples and improve manifold structures within each mini-batch. This is achieved through a minimax game between the diffusion generator and a novel structure discriminator. The proposed method outperforms existing diffusion transformers (DiT) and establishes a new state-of-the-art FID of 1.58 and 2.11 on ImageNet for class-conditional image generation at resolutions of 256×256 and 512×512, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using a new way to train computer models that can generate images or other data. This new method, called SADM, helps the model learn more about the relationships between different pieces of data. It does this by playing a game with another part of the program, which tries to figure out if the generated data looks real or not. The result is that SADM can make better pictures and perform better on certain tasks than other methods. |
Keywords
* Artificial intelligence * Diffusion * Image generation
