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

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

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.

Tensorflow implementation of 'Conditional Variational Autoencoder' concept

a collection of variational autoencoders

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

NYCU Deep Learning and Practice Summer 2023

Implementing a Conditional VAE for video prediction with PyTorch