| 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 |
| 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). |
| timbmg/VAE-CVAE-MNIST |
508 |
|
0 |
0 |
over 2 years ago |
0 |
|
1 |
|
Python |
| Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch |
| matthewvowels1/Awesome-VAEs |
448 |
|
0 |
0 |
almost 5 years ago |
0 |
|
0 |
|
|
| A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models. |
| shayneobrien/generative-models |
414 |
|
0 |
0 |
over 7 years ago |
0 |
|
1 |
mit |
Jupyter Notebook |
| Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN |
| 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 |
| andersbll/autoencoding_beyond_pixels |
350 |
|
0 |
0 |
about 9 years ago |
0 |
|
11 |
mit |
Python |
| Generative image model with learned similarity measures |
| hardmaru/cppn-gan-vae-tensorflow |
316 |
|
0 |
0 |
about 10 years ago |
0 |
|
1 |
|
Python |
| Train CPPNs as a Generative Model, using Generative Adversarial Networks and Variational Autoencoder techniques to produce high resolution images. |
