Summary of Improving Diffusion Models’s Data-corruption Resistance Using Scheduled Pseudo-huber Loss, by Artem Khrapov et al.
Improving Diffusion Models’s Data-Corruption Resistance using Scheduled Pseudo-Huber Loss
by Artem Khrapov, Vadim Popov, Tasnima Sadekova, Assel Yermekova, Mikhail Kudinov
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper presents a novel approach to mitigate the vulnerability of diffusion models to outliers in training data. The authors propose an alternative diffusion loss function, dubbed pseudo-Huber loss with a time-dependent parameter, which balances robustness against outliers during early reverse-diffusion steps with fine details restoration on final steps. This method shows improved performance on corrupted datasets in both image and audio domains compared to conventional methods. By resisting dataset corruption without requiring data filtering or purification, this approach has the potential to enhance the reliability of diffusion models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make artificial intelligence more reliable by fixing a problem with how it learns from data. When AI is trained, it’s easy for mistakes to sneak in and mess up the results. The authors came up with a new way to teach AI that makes it better at ignoring these mistakes. This means AI will be more accurate and trustworthy when making predictions or generating new content. |
Keywords
» Artificial intelligence » Diffusion » Loss function
