Displaying 1 to 8 from 8 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.

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.




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.

spatial_gan - Spatial Generative Adversarial Networks

  •    Python

The code was tested on top of Lasagne (version 0.2.dev1) and Theano (0.9.0dev2). SGANs can generate sample textures of arbitrary size that look strikingly similar - but not exactly the same - compared to a single (or several) source image(s).