| wiseodd/generative-models |
6,010 |
|
0 |
0 |
about 7 years ago |
0 |
|
18 |
unlicense |
Python |
| Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. |
| hwalsuklee/tensorflow-generative-model-collections |
3,570 |
|
0 |
0 |
over 7 years ago |
0 |
|
22 |
apache-2.0 |
Python |
| Collection of generative models in Tensorflow |
| thu-ml/zhusuan |
2,139 |
|
0 |
0 |
over 3 years ago |
0 |
|
11 |
mit |
Python |
| A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow |
| PacktPublishing/Advanced-Deep-Learning-with-Keras |
1,534 |
|
0 |
0 |
about 3 years ago |
0 |
|
3 |
mit |
Python |
| Advanced Deep Learning with Keras, published by Packt |
| jaanli/variational-autoencoder |
1,104 |
|
0 |
0 |
over 4 years ago |
0 |
|
|
mit |
Python |
| Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow) |
| altosaar/variational-autoencoder |
1,049 |
|
0 |
0 |
over 4 years ago |
0 |
|
1 |
mit |
Python |
| Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow) |
| timsainb/tensorflow2-generative-models |
833 |
|
0 |
0 |
over 5 years ago |
0 |
|
7 |
|
Jupyter Notebook |
| Implementations of a number of generative models in Tensorflow 2. GAN, VAE, Seq2Seq, VAEGAN, GAIA, Spectrogram Inversion. Everything is self contained in a jupyter notebook for easy export to colab. |
| ikostrikov/TensorFlow-VAE-GAN-DRAW |
569 |
|
0 |
0 |
almost 9 years ago |
0 |
|
8 |
apache-2.0 |
Python |
| A collection of generative methods implemented with TensorFlow (Deep Convolutional Generative Adversarial Networks (DCGAN), Variational Autoencoder (VAE) and DRAW: A Recurrent Neural Network For Image Generation). |
| aspuru-guzik-group/chemical_vae |
383 |
|
0 |
0 |
over 3 years ago |
0 |
|
36 |
apache-2.0 |
Python |
| Code for 10.1021/acscentsci.7b00572, now running on Keras 2.0 and Tensorflow |
| hwalsuklee/tensorflow-mnist-VAE |
379 |
|
0 |
0 |
about 9 years ago |
0 |
|
6 |
|
Python |
| Tensorflow implementation of variational auto-encoder for MNIST |
