Machine learning

Diffusion Model

A diffusion model is a generative deep-learning method, introduced by Ho, Jain and Abbeel in 2020 (DDPM), that learns to produce high-quality images, audio and molecular structures by reversing a step-by-step noising process. It has largely displaced GANs as the current state of the art in generative modelling.

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Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. link ↗
Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. CVPR. link ↗

Referenced by

Explainable GAN Generative Adversarial Network Normalizing Flows Variational Autoencoder Vision Transformer Wasserstein GAN Weakly Supervised Diffusion Model Weakly supervised GAN

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