Summary of Cpsample: Classifier Protected Sampling For Guarding Training Data During Diffusion, by Joshua Kazdan et al.
CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion
by Joshua Kazdan, Hao Sun, Jiaqi Han, Felix Petersen, Stefano Ermon
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 CPSample, a method that modifies the sampling process of diffusion models to prevent them from exactly replicating their training data while preserving image quality. This is achieved by training a classifier to overfit on random binary labels attached to the training data and using classifier guidance to steer the generation process away from the set of points that can be classified with high certainty, which includes the training data. The method achieves FID scores of 4.97 and 2.97 on CIFAR-10 and CelebA-64, respectively, without producing exact replicates of the training data. CPSample is computationally cheaper than retraining a diffusion model and provides greater robustness against membership inference attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CPSample is a new way to make diffusion models produce more unique images by changing how they sample new data. This is important because current methods that try to stop the models from copying their training data often lower image quality. CPSample uses a classifier that’s trained to recognize random labels on the training data, and then it uses this classifier to guide the sampling process away from areas where the model can easily copy its training data. The results show that CPSample produces high-quality images without copying the training data. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Inference
