Summary of Exploring Diffusion Models’ Corruption Stage in Few-shot Fine-tuning and Mitigating with Bayesian Neural Networks, by Xiaoyu Wu et al.
Exploring Diffusion Models’ Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networks
by Xiaoyu Wu, Jiaru Zhang, Yang Hua, Bohan Lyu, Hao Wang, Tao Song, Haibing Guan
First submitted to arxiv on: 30 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 A recent breakthrough in diffusion models (DMs) has made few-shot fine-tuning a reality, enabling personalized AI applications while reducing training costs. However, researchers have observed an unexpected phenomenon during the training process: image fidelity initially improves, then deteriorates with the emergence of noisy patterns before recovering later with severe overfitting. This corruption stage can be attributed to a narrowed learning distribution inherent in few-shot fine-tuning. To address this issue, Bayesian Neural Networks (BNNs) are applied to DMs using variational inference to broaden the learned distribution. Experimental results demonstrate that this approach significantly mitigates corruption, improving the fidelity, quality, and diversity of generated images in object-driven and subject-driven generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Diffusion models have made it possible for AI applications to be personalized while reducing training costs. But there’s a problem – during training, images get worse before they get better! Scientists have figured out what causes this issue and found a way to fix it. They used something called Bayesian Neural Networks to help the model learn better. This approach is great because it doesn’t add extra steps or complexity to the process. In fact, it works well with existing methods. The results show that this new approach makes the generated images much better. |
Keywords
» Artificial intelligence » Diffusion » Few shot » Fine tuning » Inference » Overfitting
