Collection of generative models in Tensorflow

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

Tensorflow implementation of conditional variational auto-encoder for MNIST

Learning cell communication from spatial graphs of cells

Simple and clean implementation of Conditional Variational AutoEncoder (CVAE) using PyTorch

DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents

Conditional out-of-distribution prediction

Learning informed sampling distributions and information gains for efficient exploration planning.

The official implementation of "Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting" presented in ECCV2022.

Official code for AAAI 2023 paper "Multi-stream Representation Learning for Pedestrian Trajectory Prediction"

Official project of DiverseSampling (ACMMM2022 Paper)

👾 Malware Classification using Deep Learning and Cuckoo Sandbox

PyTorch implementation of the conditional variational autoencoder (CVAE) from CodeSLAM

Black-box Few-shot Knowledge Distillation

Code for Generalization Guarantees for (Multi-Modal) Imitation Learning

CVAE-GPT2 open-domain chatbot

NCTU(NYCU) Deep Learning and Practice Spring 2021

An interactive demonstration of using a deep conditional variational autoencoder to generate synthetic MNIST style handwriting digit

Implementation of the Conditional Variational Auto-Encoder (CVAE) in Tensorflow

The implementation of Gumbel softmax reparametrization trick for discrete VAE