We have collection of more than 1 Million open source products ranging from Enterprise product to
small libraries in all platforms. We aggregate information from all open source repositories.
Search and find the best for your needs. Check out projects section.
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.
This 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.
This 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.
PaddleGAN 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.
Use 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.
Most 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.
Recent 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.