TF 2 examples: https://github.com/YunYang1994/TensorFlow2.0-Examples

ml5.js, Javascript interface to TF: https://ml5js.org/

Toward multimodal image-to-image translation (with code): https://junyanz.github.io/BicycleGAN/

Cities with electronic circuits: https://www.rileynwong.com/blog/2019/3/5/circuit-cities-with-pix2pix-using-image-to-image-translation-with-generative-adversarial-networks-to-create-buildings-maps-and-satellite-images-from-circuit-boards

Invisible cities: https://opendot.github.io/ml4a-invisible-cities/implementation/

Architecture: https://developer.nvidia.com/blog/archigan-generative-stack-apartment-building-design/

Generate semantic images (paper): https://arxiv.org/pdf/1906.12195.pdf

Neural Cities (Jasper van Loenen): https://jaspervanloenen.com/neural-cities/

City2city: https://bland.website/city2city/

https://awesomeopensource.com/projects/pix2pix

https://www.makery.info/2017/04/25/intelligence-artificielle-top-10-des-protos-a-suivre/

https://blog.eduonix.com/artificial-intelligence/grand-finale-applications-gans/

Google Autodraw: https://www.autodraw.com/

https://ml4a.github.io/guides/Pix2Pix/

https://medium.com/@dongphilyoo/how-to-train-pix2pix-model-and-generating-on-the-web-with-ml5-js-87d879fb4224

Memo Akten pix2pix with webcam: https://github.com/memo/webcam-pix2pix-tensorflow

Image to image:

• Article: https://affinelayer.com/pixsrv/
• Code: https://github.com/skabbit/pix2pix-webcam

CycleGAN (PyTorch): https://github.com/junyanz/CycleGAN

Cycle GAN in Tensorflow: https://github.com/leehomyc/cyclegan-1

Another implementation of CycleGAN: https://github.com/vanhuyz/CycleGAN-TensorFlow

Pix2pix HD: https://github.com/NVIDIA/pix2pixHD

GAN compression for Pix2pix: https://github.com/mit-han-lab/gan-compression

IGAN: https://github.com/junyanz/iGAN

DeepFace:

• Code: https://github.com/yuanxiaosc/DeepNude-an-Image-to-Image-technology
• Online demo: http://www.geometrylearning.com:3000/index_EN_621.html

DeepNude:

• https://github.com/yuanxiaosc/DeepNude-an-Image-to-Image-technology/tree/master/DeepNude_software_itself
• With PyTorch, no watermark: https://github.com/zhengyima/DeepNude_NoWatermark_withModel

Unsupervised, MUNIT: https://github.com/taki0112/MUNIT-Tensorflow

Unsupervised, UNIT: https://github.com/mingyuliutw/UNIT

Collection of GAN implementations: https://github.com/tjwei/GANotebooks

Unity:

• Pix2pix: https://github.com/keijiro/Pix2Pix
• Generative cities: https://github.com/DerTarchin/Pix2Pix-Generative-Cities

Visual Objects Network (3D shapes): https://github.com/junyanz/VON

Next frame prediction: https://coxlab.github.io/prednet/

Nvidia AI generated game (vid2vid):

• Article: https://www.theverge.com/2018/12/3/18121198/ai-generated-video-game-graphics-nvidia-driving-demo-neurips
• Code: https://github.com/NVIDIA/vid2vid

Video generation from single image: https://github.com/malfusion/pix2pix-video-synthesis

Deblurring: https://github.com/KupynOrest/DeblurGAN

Monocular image to depth map: https://github.com/gautam678/Pix2Depth

Generating visual effects (with prebuilt executable): https://github.com/keijiro/Ngx

Movie colorizing: https://github.com/awjuliani/Pix2Pix-Film

Dehazing (paper): https://openaccess.thecvf.com/content_CVPR_2019/papers/Qu_Enhanced_Pix2pix_Dehazing_Network_CVPR_2019_paper.pdf

Labeled flowers: https://www.kaggle.com/alxmamaev/flowers-recognition/data

Page to page (with demo): https://github.com/lquirosd/P2PaLA

Handwriting synthesis:

• Demo: http://www.cs.toronto.edu/~graves/handwriting.html
• Code: https://github.com/szcom/rnnlib

Text to image synthesis: https://github.com/zsdonghao/im2txt2im

Kaggle: https://www.kaggle.com/datasets

Spell (has a free plan): https://spell.ml/

Twilio (pay as you go): https://www.twilio.com/