Generative Models
Deep Belief Network
A Deep Belief Network (DBN) is a multi-layer generative graphical model. DBNs have bi-directional connections (RBM-type connections) on the top layer while the bottom layers only have top-down connections. They are trained using layerwise pre-training. Pre-training occurs by training the network component by component bottom up: treating the first two layers as an RBM and training, then treating the second layer and third layer as another RBM and training for those parameters.
Source: Origins of Deep Learning
Image Source: Wikipedia
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| General Classification | 18 | 11.92% |
| Deep Learning | 12 | 7.95% |
| Classification | 11 | 7.28% |
| Image Classification | 10 | 6.62% |
| Time Series Analysis | 5 | 3.31% |
| Dimensionality Reduction | 5 | 3.31% |
| Denoising | 4 | 2.65% |
| Speech Recognition | 4 | 2.65% |
| Object Recognition | 4 | 2.65% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
