shaohua0116 / Vae Tensorflow
Licence: mit
A Tensorflow implementation of a Variational Autoencoder for the deep learning course at the University of Southern California (USC).
Stars: ✭ 117
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Variational Autoencoder in Tensorflow
This is an Tensorflow implementation of a variational autoencoder for the deep learning course at USC (CSCI-599 Deep Learning and its Applications) taught by Professor Joseph Lim. The slides of this lecture are available here. This demo code is written by Shao-Hua Sun.
Results
Reconstruction
Generation
Transformation
Latent space
Related works
VAE
- The original VAE paper: Auto-Encoding Variational Bayes
- Variational Autoencoder: Intuition and Implementation
- Generating Large Images from Latent Vectors
- This demo code is partially based on the code from this post Variational Autoencoder in TensorFlow
Generative models
- My implementation of Semi-supervised learning GAN
- My implementation of Deep Convolutional GAN
- My implementation of Generative Latent Optimization
Author
Shao-Hua Sun / @shaohua0116 @ Joseph Lim's research lab @ USC
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