**VQ-VAE-2** is a type of variational autoencoder that combines a a two-level hierarchical VQ-[VAE](https://paperswithcode.com/method/vae) with a self-attention autoregressive model ([PixelCNN](https://paperswithcode.com/method/pixelcnn)) as a prior. The encoder and decoder architectures are kept simple and light-weight as in the original [VQ-VAE](https://paperswithcode.com/method/vq-vae), with the only difference that hierarchical multi-scale latent maps are used for increased resolution.

Please enter a description about the method here

SSFG regularization

SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks

VQ-VAE-2

Generating Diverse High-Fidelity Images with VQ-VAE-2

**ConViT** is a type of [vision transformer](https://paperswithcode.com/method/vision-transformer) that uses a gated positional self-attention module ([GPSA](https://paperswithcode.com/method/gpsa)), a form of positional self-attention which can be equipped with a “soft” convolutional inductive bias. The GPSA layers are initialized to mimic the locality of convolutional layers, then each attention head is given the freedom to escape locality by adjusting a gating parameter regulating the attention paid to position versus content information.

Source	Generating Diverse High-Fidelity Images with VQ-VAE-2
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com