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 | 97 | 14.22% |
| Denoising | 74 | 10.85% |
| Video Generation | 21 | 3.08% |
| Text-to-Image Generation | 21 | 3.08% |
| Decoder | 13 | 1.91% |
| Language Modelling | 12 | 1.76% |
| Super-Resolution | 12 | 1.76% |
| 3D Generation | 10 | 1.47% |
| Text to 3D | 9 | 1.32% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
