Generative Models
Variational Autoencoder
Introduced by Kingma et al. in Auto-Encoding Variational BayesA Variational Autoencoder is a type of likelihood-based generative model. It consists of an encoder, that takes in data $x$ as input and transforms this into a latent representation $z$, and a decoder, that takes a latent representation $z$ and returns a reconstruction $\hat{x}$. Inference is performed via variational inference to approximate the posterior of the model.
Source: Auto-Encoding Variational Bayes
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Decoder | 56 | 8.28% |
| Image Generation | 36 | 5.33% |
| Disentanglement | 32 | 4.73% |
| Denoising | 20 | 2.96% |
| Quantization | 14 | 2.07% |
| Image Classification | 13 | 1.92% |
| Language Modelling | 12 | 1.78% |
| Text Generation | 12 | 1.78% |
| Clustering | 11 | 1.63% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
