Implementation of a Deep Convolutional Generative Adversarial Network to generate realistic MNIST digits at 64x64 resolution in Tensorflow.
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Updated
Jan 29, 2017 - Jupyter Notebook
Implementation of a Deep Convolutional Generative Adversarial Network to generate realistic MNIST digits at 64x64 resolution in Tensorflow.
Generative Adversarial Networks implementation in Chainer
Various Preprocessing tools for use with Generative Adversarial Networks
Image Completion with Deep Learning in TensorFlow
Image completion with Torch
Resources and Implementations of Generative Adversarial Nets which are focusing on how to stabilize training process and generate high quality images: DCGAN, WGAN, EBGAN, BEGAN, etc.
Project 5
Generate Faces using GANs (Part of Udacity's DLFND)
A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks" - (EASY to READ)
This is the final project of the deep learning foundations course of udacity.
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