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 |
|---|---|---|
| Response Generation | 7 | 6.09% |
| Trajectory Prediction | 5 | 4.35% |
| Semantic Segmentation | 5 | 4.35% |
| Dialogue Generation | 5 | 4.35% |
| Image Segmentation | 4 | 3.48% |
| Disentanglement | 4 | 3.48% |
| General Classification | 3 | 2.61% |
| Text Generation | 3 | 2.61% |
| Classification | 2 | 1.74% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
