Summary of See-dpo: Self Entropy Enhanced Direct Preference Optimization, by Shivanshu Shekhar et al.
SEE-DPO: Self Entropy Enhanced Direct Preference Optimization
by Shivanshu Shekhar, Shreyas Singh, Tong Zhang
First submitted to arxiv on: 6 Nov 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 Direct Preference Optimization (DPO) method has been effective in aligning large language models with human preferences and improving text-to-image diffusion models. However, DPO-based methods are prone to overfitting and reward hacking when trained for prolonged periods. To address this issue, the authors propose a self-entropy regularization mechanism that enhances DPO training by encouraging exploration and robustness. This technique mitigates reward hacking, leading to improved image quality and stability across the latent space. The results demonstrate that integrating human feedback with self-entropy regularization can significantly boost image diversity and specificity, achieving state-of-the-art performance on key image generation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DPO is a way to make large language models and text-to-image models better by using what people like. Right now, this method has some problems when it’s used for a long time, like making the model too good at doing something one way but not being able to do other things well. To fix this, scientists came up with an idea to add a special “self-entropy” rule to DPO that helps it explore and learn more broadly. This rule makes the model less likely to get stuck in one spot and more likely to find new and interesting ways to create images. When people tried this new method, they found that it made the images better and more diverse than before. |
Keywords
» Artificial intelligence » Image generation » Latent space » Optimization » Overfitting » Regularization
