Summary of Self-guided Generation Of Minority Samples Using Diffusion Models, by Soobin Um and Jong Chul Ye
Self-Guided Generation of Minority Samples Using Diffusion Models
by Soobin Um, Jong Chul Ye
First submitted to arxiv on: 16 Jul 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 novel approach presented in this paper generates minority samples that reside on low-density regions of a data manifold. Built upon diffusion models, the framework leverages guided sampling to incorporate an arbitrary energy-based guidance during inference time. The key feature is its self-contained nature, requiring only a pretrained model for implementation. Unlike existing techniques, this sampler doesn’t rely on external classifiers or expensive additional components. Instead, it estimates the likelihood of features within intermediate latent samples by evaluating a reconstruction loss w.r.t. their posterior mean and then minimizes the estimated likelihood to encourage minority feature emergence. Time-scheduling techniques are also provided to manage guidance influence over inference steps. Experimental results demonstrate significant improvement in creating realistic low-likelihood minority instances using this approach, without relying on costly additional elements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates new ways to make small groups of data appear in places where they are hard to find. They use a special type of computer model called a diffusion model to do this. The big idea is that the model can look at how likely it is for certain features to appear and then try to make those features show up more often. This helps create realistic-looking small groups of data that are harder to find in the first place. The paper also shows some tricks for making sure the model doesn’t get too stuck on trying to make these small groups appear, which can be helpful. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Inference » Likelihood
