Generative Models


Introduced by Razavi et al. in Generating Diverse High-Fidelity Images with VQ-VAE-2

VQ-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


Paper Code Results Date Stars


Task Papers Share
Image Generation 2 20.00%
Quantization 1 10.00%
Change Detection 1 10.00%
Motion Planning 1 10.00%
Object Detection 1 10.00%
Object Tracking 1 10.00%
Scene Change Detection 1 10.00%
Scene Understanding 1 10.00%
Image Reconstruction 1 10.00%


Component Type
Generative Models