Summary of Efficient Training with Denoised Neural Weights, by Yifan Gong et al.
Efficient Training with Denoised Neural Weights
by Yifan Gong, Zheng Zhan, Yanyu Li, Yerlan Idelbayev, Andrey Zharkov, Kfir Aberman, Sergey Tulyakov, Yanzhi Wang, Jian Ren
First submitted to arxiv on: 16 Jul 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 paper introduces a novel approach to weight initialization in deep neural networks, leveraging generative adversarial networks (GANs) and diffusion models to synthesize weights. The authors collect a dataset of model weights spanning various image editing concepts and use it to train a weight generator. This generator is then used to initialize the weights for an image-to-image translation task, achieving a 15x acceleration in training time compared to traditional methods while maintaining improved image generation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps reduce the cost of training deep neural networks by developing a weight generator that can synthesize model weights. By using GANs and diffusion models, the authors create a dataset of model weights for various image editing concepts and train a generator that predicts these weights. This approach can accelerate training time while maintaining good performance. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Translation
