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pix2pix - Image-to-image translation with conditional adversarial nets
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.
computer-vision computer-graphics gan pix2pix dcgan generative-adversarial-network deep-learning image-generation image-manipulation image-to-image-translationiGAN - Interactive Image Generation via Generative Adversarial Networks
[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.
generative-adversarial-network image-manipulation computer-graphics computer-vision gan pix2pix dcgan deep-learningDCGAN-tensorflow - A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"
Tensorflow implementation of Deep Convolutional Generative Adversarial Networks which is a stabilize Generative Adversarial Networks. The referenced torch code can be found here.
tensorflow dcgan gan generative-modelGAN - Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN
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.
gan dcgan wasserstein-gan infogan adversarial-netscontext-encoder - [CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs
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.
image-inpainting context-encoders unsupervised-learning machine-learning generative-adversarial-network deep-learning computer-vision gan dcgan computer-graphicspycadl - Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"
This package is part of the Kadenze Academy program Creative Applications of Deep Learning w/ TensorFlow. from cadl import and then pressing tab to see the list of available modules.
deep-learning neural-network tutorial mooc gan vae vae-gan pixelcnn wavenet magenta nsynth tensorflow celeba cyclegan dcgan word2vec glove autoregressive conditional coursechainer-gan-lib - Chainer implementation of recent GAN variants
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.
deep-learning generative-adversarial-network dcgan wgan-gpUnsupervised-Anomaly-Detection-with-Generative-Adversarial-Networks - Unsupervised Anomaly Detection with Generative Adversarial Networks on MIAS dataset
Unsupervised Anomaly Detection with Generative Adversarial Networks on MIAS dataset
deep-learning dcgan gan anomaly-detectionsemantic_image_inpainting - Semantic Image Inpainting
* indicating equal contributions. Implementation of proposed cost function and backpropogation to input.
tensorflow inpaint image-processing dcgan gan deep-learning generative-adversarial-network generative-modeltensorflow-cdcgan - A short Conditional DCGAN tensorflow implementation.
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.
tensorflow dcgan mnist machinelearning shabteamMAX-Image-Completer - Recognize and extract faces in an image and complete the corrupted portions
This repository contains code to instantiate and deploy an image completer model. The goal is to fill in missing or corrupted parts of an image. This model uses Deep Convolutional Generative Adversarial Networks (DCGAN) to fill the missing regions in an image. The model is trained on the celebA dataset and works best for completing corrupted portions of a human face. Input to the model is an image containing a single corrupted face. The OpenFace face recognition tool will detect and extract the corrupted face from the input image. This extracted face is then passed to the OpenFace alignment tool where it is aligned (inner eyes with bottom lip) and resized (64 x 64) producing an output that can be used by the model to fill the corrupted portions. The output is a collage of 20 images, in a 4x5 grid, representing the intermediate results and final completed image (bottom-right). The model is based on the Tensorflow implementation of DCGAN. The model weights are hosted on IBM Cloud Object Storage. The code in this repository deploys the model as a web service in a Docker container. This repository was developed as part of the IBM Developer Model Asset Exchange.
docker-image machine-learning machine-learning-models tensorflow dcgan-tensorflow dcganprediction_gan - PyTorch Impl. of Prediction Optimizer (to stabilize GAN training)
This is a PyTorch implementation of 'prediction method' introduced in the following paper ... You can find more images at the following issues.
gan dcgan optimizer pytorch stabilizing-adversarial-nets prediction-methods pytorch-impltf-exercise-gan - Tensorflow implementation of different GANs and their comparisions
Tensorflow implementation of different GANs and their comparisions
gan gan-implementations evaluation celeba mnist madgan dcgan began wgan gang-of-gansDCGAN-Sign-Language - Generating sign language images with DCGAN using our own Sign Language Dataset
Generating sign language images with DCGAN using our own Sign Language Dataset.
keras tensorflow gan dcgan deep-learning machine-learningCatDCGAN - A DCGAN that generate Cat pictures 🐱💻
A DCGAN that generate Cat pictures 🐱💻
dcgan dcgan-tensorflow deep-learning cats tensorflow ganGAN-Project-2018 - GAN in Tensorflow to be run via Linux command line
If you are sure all the libraries in requirements.txt are installed, go to $ python main.py ...
dcgan dcgan-tensorflow dcgan-mnist-tutorial tensorflow tensorflow-models linux-kernel generative-adversarial-network gan generative-adversarial-networksDCGAN-tensorflow-slim - Implementation of DCGAN in TensorFlow-Slim
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 tensorflowGAN_Theories - 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
All have been tested with python2.7+ and tensorflow1.0+ in linux. The generated results are shown in the end of this page.
gan-theories wgan dcgan began ebgan
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