Likelihood-Based 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 |
|---|---|---|
| Image Generation | 4 | 66.67% |
| Image Reconstruction | 1 | 16.67% |
| Super-Resolution | 1 | 16.67% |
PixelCNN
