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 | 55 | 8.33% |
| Image Generation | 37 | 5.61% |
| Disentanglement | 31 | 4.70% |
| Denoising | 20 | 3.03% |
| Quantization | 14 | 2.12% |
| Language Modelling | 12 | 1.82% |
| Text Generation | 12 | 1.82% |
| Image Classification | 12 | 1.82% |
| Time Series Analysis | 11 | 1.67% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
