AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise

1 code implementation • CVPR 2022 • Julian Wyatt, Adam Leach, Sebastian M. Schmon, Chris G. Willcocks

A secondary problem is that Gaussian diffusion fails to capture larger anomalies; therefore we develop a multi-scale simplex noise diffusion process that gives control over the target anomaly size.

Denoising Unsupervised Anomaly Detection

Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models

no code implementations • 8 Mar 2021 • Sam Bond-Taylor, Adam Leach, Yang Long, Chris G. Willcocks

Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples.