Summary of Don’t Drop Your Samples! Coherence-aware Training Benefits Conditional Diffusion, by Nicolas Dufour et al.
Don’t drop your samples! Coherence-aware training benefits Conditional diffusion
by Nicolas Dufour, Victor Besnier, Vicky Kalogeiton, David Picard
First submitted to arxiv on: 30 May 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 proposed Coherence-Aware Diffusion (CAD) model integrates coherence in conditional information into diffusion models, allowing them to learn from noisy annotations without discarding data. This is achieved by conditioning the model on both the conditional information and a coherence score that reflects the quality of the conditional information. The CAD model learns to ignore or discount the conditioning when the coherence is low, resulting in more realistic and diverse samples that respect conditional information better than models trained on cleaned datasets where samples with low coherence have been discarded. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way for machines to learn from imperfectly labeled data by using something called “coherence” scores. These scores reflect how well the labels match what they’re supposed to be, and the machine can use them to decide when to ignore bad labels. The method is called Coherence-Aware Diffusion (CAD), and it’s a new way for machines to generate things like images or text based on incomplete information. |
Keywords
* Artificial intelligence * Diffusion
