[ACL 2020] Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs

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

Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

すかすかアニメボカロデータセット。1st anime vocal dataset. Extract audio (vocal) files from video based on .ass subtitle files; manually label vocal files to characters. Will be used for PITS/VITS/Diffusion text-to-speech/SVC. 根据字幕，从视频里抽取全部语音，然后手动按角色标注。

This will be the first official public release of the VItamin code base. VItamin is a python package for producing fast gravitational wave posterior samples.

Black-box Few-shot Knowledge Distillation

Labs for 5003 Deep Learning Practice course in summer term 2021 at NYCU.

A conditional version of the "very deep variational autoencoder" proposed by Rewon Child at OpenAI (2020)

Using conditional variational autoencoders to manipulate the images

A Supervised VAE Based Gen Model for Human Motion

PyTorch implementation of various Variational Autoencoder models

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

Toy example for a Conditional Variational Autoencoder in Keras. Regresses features from automatically generated images. Useful for learning about the concept.

Reconstructing spatiotemporal data with C-VAEs.

ResNet-style Autoencoders: Implementing and training AEs, VAEs, and CVAEs on provided dataset with TSNE visualizations.

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

Conditional variational autoencoder implemented in PyTorch.

This repo contains implementations of key unsupervised learning techniques, including image compression (K-means, GMM), PCA for Eigenfaces, ICA for audio separation, and CVAE for MNIST generation. It's a resource for understanding and applying foundational algorithms.

A Simple Conditional Variation Autoencoder