musyoku / adversarial-autoencoderLinks
Chainer implementation of adversarial autoencoder (AAE)
☆257Updated 7 years ago
Alternatives and similar repositories for adversarial-autoencoder
Users that are interested in adversarial-autoencoder are comparing it to the libraries listed below
Sorting:
- Adversarially Learned Inference☆311Updated 7 years ago
- ☆201Updated 8 years ago
- Replication of Semi-Supervised Learning with Deep Generative Models☆100Updated 9 years ago
- InfoGAN: Interpretable Representation Learning☆153Updated 8 years ago
- A single jupyter notebook multi gpu VAE-GAN example with latent space algebra and receptive field visualizations.☆440Updated 6 years ago
- Code for reproducing results of NIPS 2014 paper "Semi-Supervised Learning with Deep Generative Models"☆515Updated 10 years ago
- This repository contains the code to reproduce the core results from the paper "Adversarial Variational Bayes: Unifying Variational Autoe…☆208Updated 7 years ago
- Torch implementations of various types of autoencoders☆474Updated 8 years ago
- Implementation of a Variational Auto-Encoder in Theano☆381Updated 8 years ago
- Auxiliary Classifier Generative Adversarial Networks in Keras☆213Updated 7 years ago
- code for "Adversarial Feature Learning"☆239Updated 3 years ago
- Implementation of several different types of autoencoders☆116Updated 3 years ago
- Implementation of a Variational Auto-Encoder in TensorFlow☆208Updated 8 years ago
- Generative image model with learned similarity measures☆444Updated 8 years ago
- A tensorflow implementation of google's AC-GAN ( Auxiliary Classifier GAN ).☆396Updated 8 years ago
- A wizard's guide to Adversarial Autoencoders☆432Updated 4 years ago
- Implementation of VLAE☆216Updated 7 years ago
- An implementation of VAEGAN (variational autoencoder + generative adversarial network).☆93Updated 8 years ago
- Tensorflow implementation of Adversarial Autoencoders (https://arxiv.org/abs/1511.05644)☆37Updated 7 years ago
- Wasserstein Auto-Encoders☆507Updated 7 years ago
- Learning kernels to maximize the power of MMD tests