Image Generation Models

Diffusion

Introduced by Ho et al. in Denoising Diffusion Probabilistic Models

Diffusion models generate samples by gradually removing noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).

Source: Denoising Diffusion Probabilistic Models

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Image Generation 79 10.08%
Denoising 60 7.65%
Video Generation 39 4.97%
Diversity 17 2.17%
Super-Resolution 17 2.17%
Computational Efficiency 15 1.91%
Text-to-Image Generation 14 1.79%
Decoder 12 1.53%
Image Restoration 11 1.40%

Components


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories