Playing a game of GANstruction - Helena Sarinhttps://thegradient.pub/playing-a-game-of-ganstruction/
Generative Adversarial Networks (GANs) are an exciting tool for artists, allowing them to create unique, unpredictable digital art. The GAN process simulates a game, with a Critic network and a generator network competing to create realistic images. The artist works as a curator, selecting the most interesting images produced by the generator. The author recommends using CycleGAN, a neural network architecture that transforms images from one dataset into the style of another, as it allows for high-resolution images and quick training. The author offers practical advice on using CycleGAN, such as fine-tuning models on smaller datasets and experimenting with different batch sizes. The author also emphasizes the importance of using unique, personal datasets for training. Overall, the author encourages artists to experiment with GANs and to let the unpredictability of the process inspire them to create something special.
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/
https://github.com/eriklindernoren/PyTorch-GAN
https://heartbeat.fritz.ai/introduction-to-generative-adversarial-networks-gans-35ef44f21193
https://github.com/nightrome/really-awesome-gan
https://github.com/zhangqianhui/AdversarialNetsPapers
https://github.com/io99/Resources
https://github.com/yunjey/pytorch-tutorial
https://github.com/bharathgs/Awesome-pytorch-list
https://old.reddit.com/r/MachineLearning
http://www.codingwoman.com/generative-adversarial-networks-entertaining-intro/
https://medium.com/@jonathan_hui/gan-gan-series-2d279f906e7b
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ/videos
https://www.youtube.com/watch?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&v=aircAruvnKk
https://www.youtube.com/watch?list=PLxt59R_fWVzT9bDxA76AHm3ig0Gg9S3So&v=ZzWaow1Rvho