Summary of Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models, by Lucas Berry et al.
Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models
by Lucas Berry, Axel Brando, David Meger
First submitted to arxiv on: 5 Jun 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 proposed DECU (Diffusion Ensembles for Capturing Uncertainty) framework addresses the challenge of estimating epistemic uncertainty in generative diffusion models. These models require large parameter counts and operate in high-dimensional spaces, making traditional methods impractical due to computational demands. DECU efficiently trains ensembles of conditional diffusion models using pre-trained parameters, reducing computational burden and trainable parameters. The framework also employs PaiDEs (Pairwise-Distance Estimators) to measure epistemic uncertainty by evaluating mutual information between model outputs and weights in high-dimensional spaces. Experimental results on the ImageNet dataset demonstrate DECU’s ability to capture epistemic uncertainty, particularly in under-sampled image classes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of estimating uncertainty is developed for generative diffusion models. These models are big and complex, making it hard to figure out how sure we should be about their predictions. The proposed framework, called DECU, makes it more efficient to train many different versions of these models using some pre-trained parameters. This helps reduce the amount of computation needed and the number of parameters that need to be learned. The framework also uses a new way to measure uncertainty by looking at how similar or different the model’s predictions are from each other. Tests on the ImageNet dataset show that DECU can accurately capture uncertainty, especially in cases where there is not enough data. |
Keywords
* Artificial intelligence * Diffusion
