Generative Models
Conditional Variational Auto Encoder
Introduced by Sohn et al. in Learning Structured Output Representation using Deep Conditional Generative Models
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Decoder | 19 | 11.59% |
| Diversity | 17 | 10.37% |
| Response Generation | 7 | 4.27% |
| Trajectory Prediction | 5 | 3.05% |
| Semantic Segmentation | 5 | 3.05% |
| Dialogue Generation | 5 | 3.05% |
| Image Segmentation | 4 | 2.44% |
| Disentanglement | 4 | 2.44% |
| Classification | 3 | 1.83% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
