A Supervised VAE Based Gen Model for Human Motion
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Updated
May 25, 2024 - Python
A Supervised VAE Based Gen Model for Human Motion
ResNet-style Autoencoders: Implementing and training AEs, VAEs, and CVAEs on provided dataset with TSNE visualizations.
Reconstructing Spatiotemporal Data with C-VAEs
PyTorch implementation of various Variational Autoencoder models
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。
Conditional Variational Autoencoder (CVAE) implementation in JAX (accelerated).
Conditional Variational AutoEncoder (CVAE) PyTorch implementation
Using conditional variational autoencoders to manipulate the images
Conditional variational autoencoder implemented in PyTorch.
[ACL 2020] Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs
Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits
A conditional version of the "very deep variational autoencoder" proposed by Rewon Child at OpenAI (2020)
Black-box Few-shot Knowledge Distillation
Labs for 5003 Deep Learning Practice course in summer term 2021 at NYCU.
Toy example for a Conditional Variational Autoencoder in Keras. Regresses features from automatically generated images. Useful for learning about the concept.
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.
A Simple Conditional Variation Autoencoder
Bayesian based machine learning implementations (GMM, VAE & conditional VAE).
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