Image Generation Models
Diffusion
Introduced by Ho et al. in Denoising Diffusion Probabilistic ModelsDiffusion 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 | 119 | 15.80% |
| Denoising | 90 | 11.95% |
| Video Generation | 26 | 3.45% |
| Super-Resolution | 24 | 3.19% |
| Text to 3D | 21 | 2.79% |
| Text-to-Image Generation | 19 | 2.52% |
| Language Modelling | 16 | 2.12% |
| Large Language Model | 14 | 1.86% |
| Semantic Segmentation | 13 | 1.73% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
