Summary of Denoising Task Difficulty-based Curriculum For Training Diffusion Models, by Jin-young Kim et al.
Denoising Task Difficulty-based Curriculum for Training Diffusion Models
by Jin-Young Kim, Hyojun Go, Soonwoo Kwon, Hyun-Gyoon Kim
First submitted to arxiv on: 15 Mar 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 study investigates the relative difficulties of denoising tasks in diffusion-based generative models across various timesteps and noise levels. The research finds that earlier timesteps present more challenging tasks characterized by slower convergence and higher relative entropy, indicating increased task difficulty. To address this challenge, the authors introduce an easy-to-hard learning scheme inspired by curriculum learning, which organizes timesteps or noise levels into clusters and trains models with ascending orders of difficulty. This approach facilitates an order-aware training regime that progresses from easier to harder denoising tasks, leading to improved performance and faster convergence in image generation tasks, including unconditional, class-conditional, and text-to-image generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Denoising is a key task in generative modeling. Researchers have been trying to figure out which tasks are the most difficult. Some think that earlier timesteps are harder, while others believe it’s the later ones. To solve this mystery, scientists looked at how models change and how easy or hard they find different tasks. They found that earlier timesteps are actually more challenging. To make training easier, they came up with a new way of teaching models called curriculum learning. This approach helps models learn by starting with easy tasks and gradually moving on to harder ones. The result is better performance and faster training in generating images. |
Keywords
» Artificial intelligence » Curriculum learning » Diffusion » Image generation
