Tensorflow implementation of conditional variational auto-encoder for MNIST

Code for "MojiTalk: Generating Emotional Responses at Scale" https://arxiv.org/abs/1711.04090

Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

Diverse Image Captioning with Context-Object Split Latent Spaces (NeurIPS 2020)

Latent Normalizing Flows for Many-to-Many Cross Domain Mappings (ICLR 2020)

Pytorch implementation for VAE and conditional VAE.

Conditional Latent Autoregressive Recurrent Model for spatiotemporal learning

Tensorflow implementation of 'Conditional Variational Autoencoder' concept

a collection of variational autoencoders

Code for PulseBat dataset. We use conditional variational autoencoder to generate sufficient pulse voltage response data across random battery SOC retirement conditions, facilitating rapid, accurate and sustainable downstream SOH estimation tasks.

generate arbitrary handwritten letter/digits based on the inputs

PyTorch implementation of various Variational Autoencoder models

Deep Learning & Labs Course, NYCU, 2023

The computing scripts associated with our paper entitled "Oversampling Highly Imbalanced Indoor Positioning Data using Deep Generative Models".

Bayesian based machine learning implementations (GMM, VAE & conditional VAE).

Generative models nano version for fun. No STOA here, nano first.

Implementing a Conditional VAE for video prediction with PyTorch

A PyTorch implementation of neural dialogue system using conditional variational autoencoder (CVAE)

NYCU Deep Learning and Practice Summer 2023