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 | 102 | 14.05% |
| Denoising | 97 | 13.36% |
| Text-to-Image Generation | 27 | 3.72% |
| Video Generation | 19 | 2.62% |
| Super-Resolution | 16 | 2.20% |
| 3D Generation | 13 | 1.79% |
| Text to 3D | 13 | 1.79% |
| Language Modelling | 11 | 1.52% |
| Semantic Segmentation | 11 | 1.52% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
