Summary of Your Diffusion Model Is Secretly a Noise Classifier and Benefits From Contrastive Training, by Yunshu Wu et al.
Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training
by Yunshu Wu, Yingtao Luo, Xianghao Kong, Evangelos E. Papalexakis, Greg Ver Steeg
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 approach in this paper revisits the diffusion sampling process to address sample quality degradation caused by poorly estimated denoisers in regions outside the training distribution. A self-supervised training objective is introduced that differentiates the levels of noise added to a sample, leading to improved OOD denoising performance. The method is based on the observation that diffusion models define a log-likelihood ratio that distinguishes distributions with different amounts of noise, and this expression depends on denoiser performance outside the standard training distribution. This leads to improved performance and speed for parallel samplers in both sequential and parallel settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper fixes a problem in diffusion models that makes it hard to generate good samples from new data. When we try to make more samples, our model gets confused because it doesn’t know how to denoise the noise in those samples. To solve this, the researchers came up with a new way of training their model that helps it learn how to remove noise better. This makes it possible to generate many good samples at once, which is important for lots of applications. |
Keywords
» Artificial intelligence » Diffusion » Log likelihood » Self supervised
