Summary of Enhancing Consistency-based Image Generation Via Adversarialy-trained Classification and Energy-based Discrimination, by Shelly Golan et al.
Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based Discrimination
by Shelly Golan, Roy Ganz, Michael Elad
First submitted to arxiv on: 25 May 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 The proposed technique uses a joint classifier-discriminator model to enhance the perceptual quality of images generated by Consistency models. These models overcome the slowness of diffusion algorithms by directly mapping noise to data, but typically fall short in sample quality compared to their diffusion origins. The joint model is trained adversarially, with the classifier grading images based on their assignment to a designated class and the discriminator assessing the proximity of the input image to the targeted data manifold. This approach refines synthesized images using example-specific projected gradient iterations under the guidance of the joint machine, achieving improved FID scores on the ImageNet 64×64 dataset for both Consistency-Training and Consistency-Distillation techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make Consistency models better at creating high-quality images. These models are fast but not as good as others that take longer to work. The new method uses two parts, one that looks at the image and says if it’s good or bad, and another part that checks how close the image is to what it should be like. This helps make the images even better. It works well for both kinds of Consistency models and makes them more accurate on a big dataset. |
Keywords
» Artificial intelligence » Diffusion » Distillation
