Summary of Understanding Hallucinations in Diffusion Models Through Mode Interpolation, by Sumukh K Aithal et al.
Understanding Hallucinations in Diffusion Models through Mode Interpolation
by Sumukh K Aithal, Pratyush Maini, Zachary C. Lipton, J. Zico Kolter
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This research paper investigates a phenomenon known as mode interpolation in diffusion-based image generation models. Mode interpolation occurs when the model smoothly interpolates between nearby data modes, generating samples that are outside the original training distribution’s support, effectively “hallucinating” new information not present in the training data. The study reveals that the discontinuous loss landscape in the decoder of the diffusion model is responsible for this phenomenon and can be captured using a simple metric to measure variance in the generated sample trajectory. The researchers demonstrate the removal of over 95% of hallucinations while retaining 96% of in-support samples, with implications for recursive training on synthetic data. Experiments were conducted on MNIST and 2D Gaussians datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper looks at a type of problem that can happen when creating artificial images using a special kind of computer program called a diffusion model. Sometimes, these models create fake details or objects that didn’t exist in the training data. The researchers wanted to understand why this happens and how it can be fixed. They found that the problem is caused by the way the model is designed, but they also discovered that it’s possible to remove most of these “hallucinations” while still generating realistic images. |
Keywords
» Artificial intelligence » Decoder » Diffusion » Diffusion model » Image generation » Synthetic data
