Summary of Curriculum Direct Preference Optimization For Diffusion and Consistency Models, by Florinel-alin Croitoru et al.
Curriculum Direct Preference Optimization for Diffusion and Consistency Models
by Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, Nicu Sebe, Mubarak Shah
First submitted to arxiv on: 22 May 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 proposed Curriculum DPO method optimizes text-to-image generation by leveraging curriculum learning and ranking examples based on a reward model. This approach involves two training stages: first, generating examples for each prompt and obtaining their ranking; then, sampling increasingly difficult pairs of examples to train the generative model. The sampled pairs are split into batches according to their difficulty levels, which are gradually used to train the model. Curriculum DPO outperforms state-of-the-art fine-tuning approaches on nine benchmarks in terms of text alignment, aesthetics, and human preference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make computers generate images that match written descriptions. This is called “text-to-image generation.” The researchers came up with a new method called Curriculum DPO, which helps the computer learn how to do this better by using a special kind of learning called curriculum learning. The method works by first generating many examples and then using those examples to train the computer model. The harder examples are used first, and the easier ones later on. This approach helped the researchers’ computer model outperform other state-of-the-art models in nine different tests. |
Keywords
» Artificial intelligence » Alignment » Curriculum learning » Fine tuning » Generative model » Image generation » Prompt
