Generative Models
VQ-VAE-2
Introduced by Razavi et al. in Generating Diverse High-Fidelity Images with VQ-VAE-2VQ-VAE-2 is a type of variational autoencoder that combines a a two-level hierarchical VQ-VAE with a self-attention autoregressive model (PixelCNN) as a prior. The encoder and decoder architectures are kept simple and light-weight as in the original VQ-VAE, with the only difference that hierarchical multi-scale latent maps are used for increased resolution.
Source: Generating Diverse High-Fidelity Images with VQ-VAE-2
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Decoder | 2 | 13.33% |
| Image Generation | 2 | 13.33% |
| Quantization | 1 | 6.67% |
| Change Detection | 1 | 6.67% |
| Motion Planning | 1 | 6.67% |
| Object Detection | 1 | 6.67% |
| Object Tracking | 1 | 6.67% |
| Scene Change Detection | 1 | 6.67% |
| Scene Understanding | 1 | 6.67% |
PixelCNN
