Summary of Diffusion Curriculum: Synthetic-to-real Generative Curriculum Learning Via Image-guided Diffusion, by Yijun Liang et al.
Diffusion Curriculum: Synthetic-to-Real Generative Curriculum Learning via Image-Guided Diffusion
by Yijun Liang, Shweta Bhardwaj, Tianyi Zhou
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel approach to training deep neural networks by generating high-quality synthetic data through text-guided prompts. The study highlights the limitations of classical data augmentation and diffusion models, which can generate diverse synthetic data but lack control over the proximity of synthetic images to original images. To address this limitation, the authors introduce image guidance to achieve a spectrum of interpolations between synthetic and real images. This enables the generation of full-spectrum data that adjusts the image guidance level for each training stage. The proposed “Diffusion Curriculum (DisCL)” is applied to two challenging tasks: long-tail classification and learning from low-quality data. Experimental results demonstrate a significant gain in out-of-distribution and in-distribution macro-accuracy, with improvements of 2.7% and 2.1%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to improve the training of deep neural networks by generating synthetic data that is similar to real images. This helps the model learn better from limited or poor-quality data. The researchers found a way to control how similar these synthetic images are to the real ones, allowing them to create a wide range of images that can help or hinder the model’s performance. They then developed a new approach called “Diffusion Curriculum” that adjusts the level of similarity between synthetic and real images for each stage of training. This allows the model to learn more effectively from difficult examples. The authors tested this approach on two challenging tasks and found significant improvements in accuracy. |
Keywords
» Artificial intelligence » Classification » Data augmentation » Diffusion » Synthetic data
