Displaying 1 to 20 from 27 results

pix2pix - Image-to-image translation with conditional adversarial nets

  •    Lua

Image-to-Image Translation with Conditional Adversarial Networks Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros CVPR, 2017. On some tasks, decent results can be obtained fairly quickly and on small datasets. For example, to learn to generate facades (example shown above), we trained on just 400 images for about 2 hours (on a single Pascal Titan X GPU). However, for harder problems it may be important to train on far larger datasets, and for many hours or even days.

iGAN - Interactive Image Generation via Generative Adversarial Networks

  •    Python

[Project] [Youtube] [Paper] A research prototype developed by UC Berkeley and Adobe CTL. Latest development: [pix2pix]: Torch implementation for learning a mapping from input images to output images. [CycleGAN]: Torch implementation for learning an image-to-image translation (i.e. pix2pix) without input-output pairs. [pytorch-CycleGAN-and-pix2pix]: PyTorch implementation for both unpaired and paired image-to-image translation.

DCGAN-tensorflow - A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"

  •    Javascript

Tensorflow implementation of Deep Convolutional Generative Adversarial Networks which is a stabilize Generative Adversarial Networks. The referenced torch code can be found here.




GAN - Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

  •    Python

All have been tested with python2.7+ and tensorflow1.0+ in linux. The final layer can be sigmoid(data: [0,1]) or tanh(data:[-1,1]), my codes all use sigmoid. Using weights_initializer=tf.random_normal_initializer(0, 0.02) will converge faster.

context-encoder - [CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

  •    Lua

If you could successfully run the above demo, run following steps to train your own context encoder model for image inpainting. Features for context encoder trained with reconstruction loss.

chainer-gan-lib - Chainer implementation of recent GAN variants

  •    Python

This repository collects chainer implementation of state-of-the-art GAN algorithms. These codes are evaluated with the inception score on Cifar-10 dataset. Note that our codes are not faithful re-implementation of the original paper. This implementation has been tested with the following versions.


tf-dcgan - DCGAN implementation by TensorFlow

  •    Python

DCGAN implementation by TensorFlow

tensorflow-cdcgan - A short Conditional DCGAN tensorflow implementation.

  •    Python

This is a short implementation of a Conditional DCGAN, however if you need a cDCGAN for real-world use cases, please consider using a more serious implementation. Here can be seen a cDCGAN trained on CIFAR-10 using the same networks architectures I used for MNIST, obviously it shows that we need to be careful when designing the architecture. It works better using more filters.

DCGAN-tensorflow-slim - Implementation of DCGAN in TensorFlow-Slim

  •    Python

Implementation of DCGAN with TensorFlow slim. Base codes and models are from DCGAN in Tensorflow made by Taehoon Kim. At this time, this code only support Flower dataset, but maybe with some tweaks you can train/evaluate in other dataset. I know there are lots of code of DCGAN, especially made by Taehoon Kim. However, this code implement DCGAN with the bleeding edges features of TensorFlow such as TF-Slim, tf.train.Supervisor and TFRecords etc.

DCGAN-LSGAN-WGAN-WGAN-GP-Tensorflow - DCGAN LSGAN WGAN WGAN-GP Tensorflow

  •    Python

Tensorflow implementation of DCGAN, LSGAN, WGAN and WGAN-GP, and we use DCGAN as the network architecture in all experiments.

dcgan - Deep Convolutional Generative Adversarial Networks based on TensorFlow / TensorLayer

  •    Python

TensorFlow / TensorLayer implementation of Deep Convolutional Generative Adversarial Networks which is a stabilize Generative Adversarial Networks.

improved-gan - Chainer implementation of GAN / Deep Convolutional GAN (DCGAN) based on "Improved Techniques for Training GANs"

  •    Python

Chainer implementation of GAN / Deep Convolutional GAN (DCGAN) based on "Improved Techniques for Training GANs"





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