Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty Estimation

November 29, 2024 Β· Declared Dead Β· πŸ› IEEE Workshop/Winter Conference on Applications of Computer Vision

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Authors Michele De Vita, Vasileios Belagiannis arXiv ID 2412.00205 Category cs.CV: Computer Vision Cross-listed cs.LG, stat.ML Citations 8 Venue IEEE Workshop/Winter Conference on Applications of Computer Vision Last Checked 3 months ago
Abstract
Despite the remarkable progress in generative modelling, current diffusion models lack a quantitative approach to assess image quality. To address this limitation, we propose to estimate the pixel-wise aleatoric uncertainty during the sampling phase of diffusion models and utilise the uncertainty to improve the sample generation quality. The uncertainty is computed as the variance of the denoising scores with a perturbation scheme that is specifically designed for diffusion models. We then show that the aleatoric uncertainty estimates are related to the second-order derivative of the diffusion noise distribution. We evaluate our uncertainty estimation algorithm and the uncertainty-guided sampling on the ImageNet and CIFAR-10 datasets. In our comparisons with the related work, we demonstrate promising results in filtering out low quality samples. Furthermore, we show that our guided approach leads to better sample generation in terms of FID scores.
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