EfficientMORL

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

Advanced Deep Learning with Keras, published by Packt

Diffusion Models in Medical Imaging (Published in Medical Image Analysis Journal)

Vector Quantized VAEs - PyTorch Implementation

A collection of generative methods implemented with TensorFlow (Deep Convolutional Generative Adversarial Networks (DCGAN), Variational Autoencoder (VAE) and DRAW: A Recurrent Neural Network For Image Generation).

To learn and reason like humans, AI must first learn to factorise interpretable representations of independent data generative factors (preferably in an unsupervised manner!!). What does all this mean? Go through this tutorial to get an overview of disentanglement in the context of unsupervised visual disentangled representation learning.

Tensorflow implementation of variational auto-encoder for MNIST

Efficient Graph Generation with Graph Recurrent Attention Networks, Deep Generative Model of Graphs, Graph Neural Networks, NeurIPS 2019

Pytorch implementation of BERT4Rec and Netflix VAE.

Official implementation of "DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents"