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

Some demo VAE models implemented with pytorch, including AE, DAE, VAE, CVAE.

NYCU DLP 2023

a collection of variational autoencoders

CVAE implementation on MNIST dataset using PyTorch

A robust and unsupervised KPI anomaly detection algorithm based on conditional variational autoencoder

NYCU 深度學習與實驗 Deep Learning Spring 2024

DEPRECATED - This project implements a Conditional Variational Autoencoder (CVAE) to generate shapes conditioned on emotions, using the EmoSet dataset. It explores the intersection of emotion recognition and generative models to create visual representations based on emotional input.

Conditional Variational Autoencoder (CVAE) implementation in JAX (accelerated).

Jittor reimplementation of DiverseSampling (MM22)

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

👾 Malware Classification using Deep Learning and Cuckoo Sandbox

NCTU(NYCU) Deep Learning and Practice Spring 2021

变分自编码器、条件自变分自编码器

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

Geometry-based Molecular Generation with Deep Constrained Variational Autoencoder.

CVAE for dialog generation

Conditional Variational Auto-Encoder for generation of synthetic data for Antimicrobial Resistance.

The implementation of Gumbel softmax reparametrization trick for discrete VAE

Implementation of CVAE. Trained CVAE on faces from UTKFace Dataset to produce synthetic faces with a given degree of happiness/smileyness.