Casey Reas - Making Pictures with Generative Adversarial Networks - Machine Learning Resources
Working with GANs involves writing code, and it most often involves working with the Python programming language.[1] If this is all new to you, there is a sea of jargon and software systems to navigate. What is Tensorflow? What is Torch? What is Jupyter? Thankfully, there are initiatives to make machine learning approachable for broader audiences such as Machine Learning for Artists and ml5.js (Friendly Machine Learning for the Web). In my studio, because I wanted to go down a narrow path of working with GANs for making pictures, we developed our own ways of doing things. After trying different options, we settled on working with a dedicated computer running Linux and with a fast GPU.[2] The code we use to make pictures is modified from code posted on Github by researchers.[3] We also write Processing code to develop the thousands of images that are used for training.[4] Without the culture of academic publishing and open-source code, it wouldn’t have been possible for me to get started working in this area.
The volume of technical papers released on the topic of machine learning and GANs is daunting. It has taken months to feel comfortable with many of the fundamental ideas and terminology. It is my hope that this short book can help introduce some of the possibilities and considerations to a wider audience.
Additional resources for taking these questions further:
ARTISTS AND MACHINE INTELLIGENCE
Art in the Age of Machine Intelligence
Blaise Agüera y Arcas
ONLINE DEMOS AND VIDEOS
AI Experiments, Collection curated by Google
AI Hub, Google Research
Image-to-Image Demo, Christopher Hesse (Edges2Cats)
Style Transfer in ML5, Yining Shi
MACHINE LEARNING FOR ARTISTS
GENE KOGAN ET AL.
PAPERS
Generative Adversarial Networks,
Ian Goodfellow et al.
NIPS 2016 Tutorial: Generative Adversarial Networks,
Ian Goodfellow
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks,
Alex Radford et al.
Generative Adversarial Networks: An Overview,
Vincent Dumoulin et al.
Image-to-Image Translation with Conditional Adversarial Networks,
Phillip Isola et al.
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks,
Jun-Yan Zhu et al.
CODE