Reconstructing Spatiotemporal Data with C-VAEs
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Updated
May 23, 2024 - Jupyter Notebook
Reconstructing Spatiotemporal Data with C-VAEs
Conditional Variational Autoencoder (CVAE) implementation in JAX (accelerated).
ResNet-style Autoencoders: Implementing and training AEs, VAEs, and CVAEs on provided dataset with TSNE visualizations.
Conditional variational autoencoder implemented in PyTorch.
A Simple Conditional Variation Autoencoder
Toy example for a Conditional Variational Autoencoder in Keras. Regresses features from automatically generated images. Useful for learning about the concept.
PyTorch implementation of various Variational Autoencoder models
A Supervised VAE Based Gen Model for Human Motion
Bayesian based machine learning implementations (GMM, VAE & conditional VAE).
Using conditional variational autoencoders to manipulate the images
A conditional version of the "very deep variational autoencoder" proposed by Rewon Child at OpenAI (2020)
Labs for 5003 Deep Learning Practice course in summer term 2021 at NYCU.
Black-box Few-shot Knowledge Distillation
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.
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) PyTorch implementation
Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits
[ACL 2020] Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs
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