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-translationThis package includes CycleGAN, pix2pix, as well as other methods like BiGAN/ALI and Apple's paper S+U learning. The code was written by Jun-Yan Zhu and Taesung Park. Note: Please check out PyTorch implementation for CycleGAN and pix2pix. The PyTorch version is under active development and can produce results comparable or better than this Torch version.
gan generative-adversarial-network deep-learning image-generation image-manipulation cyclegan pix2pix gans computer-vision computer-graphics torchThis is our PyTorch implementation for both unpaired and paired image-to-image translation. It is still under active development. The code was written by Jun-Yan Zhu and Taesung Park, and supported by Tongzhou Wang.
pytorch gan cyclegan pix2pix deep-learning computer-vision computer-graphics image-manipulation image-generation generative-adversarial-network gansMMEditing is an open source image and video editing toolbox based on PyTorch. It is a part of the OpenMMLab project. The master branch works with PyTorch 1.3 to 1.6.
pytorch generative-adversarial-network image-generation super-resolution inpainting mattingPaddleGAN provides developers with high-performance implementation of classic and SOTA Generative Adversarial Networks, and supports developers to quickly build, train and deploy GANs for academic, entertainment and industrial usage. GAN-Generative Adversarial Network, was praised by "the Father of Convolutional Networks" Yann LeCun (Yang Likun) as [One of the most interesting ideas in the field of computer science in the past decade]. It's the one research area in deep learning that AI researchers are most concerned about.
resolution image-editing gan image-generation pix2pix super-resolution cyclegan motion-transfer psgan first-order-model wav2lip photo2cartoonUse a deep neural network to borrow the skills of real artists and turn your two-bit doodles into masterpieces! This project is an implementation of Semantic Style Transfer (Champandard, 2016), based on the Neural Patches algorithm (Li, 2016). Read more about the motivation in this in-depth article and watch this workflow video for inspiration. The doodle.py script generates a new image by using one, two, three or four images as inputs depending what you're trying to do: the original style and its annotation, and a target content image (optional) with its annotation (a.k.a. your doodle). The algorithm extracts annotated patches from the style image, and incrementally transfers them over to the target image based on how closely they match.
deep-neural-networks deep-learning image-processing image-manipulation image-generationSnappy is a PHP library allowing thumbnail, snapshot or PDF generation from a url or a html page. It uses the excellent webkit-based wkhtmltopdf and wkhtmltoimage.
image-generation html-to-pdf pdf-generation hacktoberfest html-to-image wkhtmltopdfTensorFlow implementation of Unsupervised Cross-Domain Image Generation.
tensorflow domain-transfer unsupervised-learning image-generationPersian Log2Vis is a library to convert all Arabic-based languages (eg. Persian/Farsi, Arabic, Dari, Pashto, Panjabi, Urdu and ...) texts to image.
persian-log2vis logical visual image image-generation logical-to-visual arabic pashto-language arabic-language pashto urdu dari panjabi farsi persian persian-language letter characterMost of the code is in core theano. 'keras' has been used for loading data. Optimizer implementation from 'lasagne' has been used. You can use experiments.sh to train the model and install_dependencies.sh to install the dependencies.
pixelcnn theano deep-learning image-generationThis repository contains implementation of VAE and beta-VAE. Following are the generated samples after 82000 iterations of training on celeb-A dataset.
vae beta-vae celeba image-generation deep-learningFor detailed explanation on how things work, check out Nuxt.js docs.
image-processing netflix image-generation nuxtjs konvajs canvasjs vue-konvaRecent multi-modal transformers have achieved tate of the art performance on a variety of multimodal discriminative tasks like visual question answering and generative tasks like image captioning. This begs an interesting question: Can these models go the other way and generate images from pieces of text? Our analysis of a popular representative from this model family - LXMERT - finds that it is unable to generate rich and semantically meaningful imagery with its current training setup. We introduce X-LXMERT, an extension to LXMERT with training refinements. X-LXMERT's image generation capabilities rival state of the art generative models while its question answering and captioning abilities remains comparable to LXMERT. Please checkout ./feature_extraction for download pre-extracted features and more details.
image-generation pretrained-models text-to-image ai2 vision-and-language emnlp2020 x-lxmertModels generated by CycleGAN. This translates bear to panda in image.
machine-learning computer-vision deep-learning neural-network gan image-generation cyclegan
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