Tensorflow implementation of conditional variational auto-encoder for MNIST
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Updated
Apr 25, 2017 - Python
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.
Tensorflow implementation of 'Conditional Variational Autoencoder' concept
generate arbitrary handwritten letter/digits based on the inputs
Conditional Latent Autoregressive Recurrent Model for spatiotemporal learning
a collection of variational autoencoders
PyTorch implementation of various Variational Autoencoder models
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).
Deep Learning & Labs Course, NYCU, 2023
A PyTorch implementation of neural dialogue system using conditional variational autoencoder (CVAE)
Generative models nano version for fun. No STOA here, nano first.
NYCU Deep Learning and Practice Summer 2023
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