└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # AI4D 2 | Workshop links 3 | 4 | ## Tools we used 5 | - [Runway Machine Learning for Creators](https://runwayml.com/) 6 | - [Teachable Machine](https://teachablemachine.withgoogle.com/) 7 | AliaksandrSiarohin/first-order-model/blob/master/demo.ipynb#scrollTo=UCMFMJV7K-ag) 8 | - How it works, [An Overview of Early Vision in InceptionV1](https://distill.pub/2020/circuits/early-vision/), [OpenAI Microscope](https://microscope.openai.com/models) 9 | - [Artbreeder](https://artbreeder.com/) 10 | - [Colab demo: first order motion](https://colab.research.google.com/github/) 11 | 12 | ## Datasets 13 | - Just search Google: [flower dataset](https://www.google.com/search?q=flower+dataset), [shoe dataset](https://www.google.com/search?q=shoe+dataset) 14 | - From your own collection: [Kooni / Yuma Kishi](https://twitter.com/obake_ai/status/1214933412181463042), [Scott Eaton](https://www.youtube.com/watch?v=h6Oi92tit7c) 15 | - [Machine Learning datasets (from papers)](https://www.datasetlist.com/) 16 | - [Awsome data (not just images)](https://github.com/awesomedata/awesome-public-datasets#imageprocessing) 17 | - [Image dataset list by Golan Levin](https://docs.google.com/spreadsheets/d/1VijZSkQbqOvsvYBXdCx9UGu5zHGZPPpzwH2uHS-2XxQ/edit#gid=0) 18 | 19 | Scraping tutorials by Derrick Schultz: 20 | - [Scraping Flickr Tags](https://www.youtube.com/watch?v=Ygsk9vMRTtg) 21 | - [Scraping Google Images](https://www.youtube.com/watch?v=X2w1oMfXYfk) 22 | - [Scraping Instagram](https://www.youtube.com/watch?v=tBmQcdLLFyc&t=243s) 23 | 24 | Chrome image downloaders (free, USE AT OWN RISK): 25 | - [Fatkun batch download image](https://chrome.google.com/webstore/detail/fatkun-batch-download-ima/nnjjahlikiabnchcpehcpkdeckfgnohf) 26 | - [Image downloader cont](https://chrome.google.com/webstore/detail/image-downloader-continue/jfkjbfhcfaoldhgbnkekkoheganchiea) 27 | 28 | Dataset resizing 29 | - [ImageMagick](https://imagemagick.org/) 30 | - [Image normalization tools, Python](https://github.com/dvschultz/dataset-tools) 31 | - [Birme online image resizer (eh)](https://www.birme.net/) 32 | - [Photoshop Batch resize images to fit into fixed dimensions](https://mattinazheng.com/2017/01/batch-resize-images-to-fit-into-fixed-dimensions-in-photoshop/) 33 | - [How to Batch Resize Your Images Quickly Using Photoshop](https://digital-photography-school.com/batch-resize-images-using-photoshop/) 34 | - [Flexxi - Batch Image Resizer - Use at own risk](https://sourceforge.net/projects/flexxi-image-resizer/) 35 | 36 | 37 | ## Runway code Examples 38 | - [Made with Runway showcase](https://runwayml.com/madewith) 39 | 40 | by Daniel Shiffman / the coding train 41 | * [The Coding Train Coding Challenge #150: AI Rainbows with RunwayML and p5.js](https://thecodingtrain.com/CodingChallenges/150-runway-rainbows.html) 42 | * [Send mouse input to RunwayML](https://editor.p5js.org/ima_ml/sketches/OUDjk3H4-) 43 | * [Send webcam video to RunwayML - HTTP](https://editor.p5js.org/ima_ml/sketches/cp87sFNRw) 44 | * [Send webcam video to RunwayML - Socket.io](https://editor.p5js.org/ima_ml/sketches/1wLmWw0XI) 45 | * [Generate StyleGAN Image](https://editor.p5js.org/ima_ml/sketches/GOiFqtbkK) 46 | * StyleGAN+Runway latent space walks [1/ Lerp loop between two images](https://editor.p5js.org/ima_ml/sketches/dyJmIybwi-) and [2/ random, constant and noise walks](https://editor.p5js.org/ima_ml/sketches/7YZzS37yh) 47 | 48 | Others P5.JS / ML5 / TF.JS demos 49 | - [Webcam Image Classification using MobileNet and p5.js](https://editor.p5js.org/yining/sketches/YXh8UG6pV) 50 | - [BigGAN send random vector + category to Runway, get image](https://editor.p5js.org/yining/sketches/kXqoZJuOf) 51 | - [Generate single StyleGAN image](https://editor.p5js.org/ima_ml/sketches/GOiFqtbkK) 52 | - [Interpolate between two StyleGAN vectors a/b](https://editor.p5js.org/ima_ml/sketches/dyJmIybwi-) 53 | - [Latent space walk: Random, Directional, Noise](https://editor.p5js.org/ima_ml/sketches/7YZzS37yh) 54 | 55 | 56 | 57 | ## Works, projects 58 | - [The huge list of creativity + machine learning works](https://mlart.co) 59 | - [Emergent Tool Use from 60 | Multi-Agent Interaction / OpenAI](https://openai.com/blog/emergent-tool-use/) 61 | - [Mosaic Virus / Anna Ridler](http://annaridler.com/mosaic-virus/) 62 | - [Eons / Mike Tyka](http://www.miketyka.com/?p=eons) 63 | - [Art Breeder / Joel Simon](https://artbreeder.com/) 64 | - [Art AI - AI art for sale](https://www.artaigallery.com/) 65 | - [AI Brushes](https://nurecas.com/ai-brushes) 66 | - [Unity Neural network generated faces from segmentation masks](https://www.youtube.com/watch?v=Ng7v9EkWXsA) 67 | 68 | 69 | ## Video 70 | - [Kooni / Yuma Kishi](https://twitter.com/obake_ai/status/1214933412181463042) 71 | - [Another one in this series ^](https://twitter.com/obake_ai/status/1176089700433448960) 72 | - [StyleGAN model to generate new portraits of non-existing people / Andreas Refsgaard](https://vimeo.com/378764538) 73 | - [FW2020 Menswear Collection / Robbie Barrat and Acne Studios](https://twitter.com/videodrome/status/1218996727191044100) 74 | - [pix2pix Learning to see: Gloomy Sunday / Memo Akten](https://vimeo.com/260612034) 75 | - [Talk: Mario Klingemann’s Neurography, Cameraless Photography with Neural Networks](https://www.youtube.com/watch?v=21W5-q5YYjw) 76 | - [Talk: Scott Eaton, Artist+AI Lecture: Figures and Form](https://www.youtube.com/watch?v=TN7Ydx9ygPo) 77 | - [Google's Experiment: Visualizing High-Dimensional Space](https://www.youtube.com/watch?v=wvsE8jm1GzE) 78 | - [Stylegan2 Experiments - Projecting real images of Johnny Depp](https://www.youtube.com/watch?v=3SXOGtTvfRQ) 79 | - [StyleGAN2 inspiration and techniques](https://youtu.be/lYoIn1aL37s) 80 | 81 | ## Experiments 82 | - [ML4A, Machine Learning for Artists Demos / Gene Kogan](https://ml4a.github.io/demos/) 83 | - [Turning perler beads into landscape fotos / Andreas Refsgaard 84 | @AndreasRef 85 | ](https://twitter.com/AndreasRef/status/1194747808559054850) 86 | - [Few-shot face translation video / @g_massol 87 | ](https://twitter.com/g_massol/status/1217495319300202496?s=11) 88 | - [Experiment with Google](https://experiments.withgoogle.com/collection/ai) 89 | - [Creativity disrupted](https://gitlab.fhnw.ch/hgk-ml/hgk-ml-seminars/tree/master/creativity-disrupted) 90 | - [Ml5.js community project](https://ml5js.org/community/) 91 | - [GauGAN experiment / Jonathan Fly](https://twitter.com/jonathanfly/status/1223042887639760896) 92 | - [Segmentation mask (from Unity) into Spade face (from Runway)](https://twitter.com/pretendsmarts/status/1189642138415517697) 93 | - [XOROMANCY Using hand tracking to walk latent space](http://www.graycrawford.com/xoromancy) 94 | - [Live Brush to GauGAN thing](https://twitter.com/fabinrasheed/status/1191255610479669248) 95 | - [Passwords.ai](https://passwords.ai/) 96 | 97 | ## Language generators 98 | - [Write With Transformer: Get a modern neural network to auto-complete your thoughts](https://transformer.huggingface.co/) 99 | - [Grover: writes fake news articles](https://grover.allenai.org/) 100 | - [Talk to transformer](https://talktotransformer.com/) 101 | 102 | 103 | ## People to follow 104 | - [Gene Kogan](https://genekogan.com/) 105 | - [Mike Tyka](http://www.miketyka.com) 106 | - [Mario Klingemann](http://quasimondo.com/) 107 | - [Scott Eaton](http://www.scott-eaton.com/) 108 | - [Peter Baylies](https://twitter.com/pbaylies) 109 | - [Derrick Schultz](https://dvschultz.github.io/design/index.html), On Yotube: [bustbright](https://www.youtube.com/channel/UCaZuPdmZ380SFUMKHVsv_AA) 110 | - [Joel Simon](http://www.joelsimon.net/) 111 | - [Philipp Schmitt](https://philippschmitt.com/) 112 | - [Memo Akten](http://www.memo.tv/) 113 | - Helena Sarin: [Neural Bricolage](https://www.neuralbricolage.com/), [On Twitter](https://twitter.com/glagolista) 114 | - [Shinseungback Kimyonghun](http://ssbkyh.com/) 115 | - [Anna Riddler](http://annaridler.com/) 116 | 117 | ## Courses 118 | - [Introduction to Synthetic Media Class at ITP/NYU](https://github.com/runwayml/Intro-Synthetic-Media) 119 | - [Introduction to Machine Learning for the Arts at IMA / Tisch / NYU.](https://github.com/ml5js/Intro-ML-Arts-IMA) 120 | - [The Neural Aesthetic @ ITP-NYU, Fall 2018 by Gene Kogan](https://ml4a.github.io/classes/itp-F18/) 121 | - [Super basic: Andrew NG's AI for everyone](https://www.deeplearning.ai/ai-for-everyone/) 122 | - [Fast.AI: Practial, requires Python know-how](https://course.fast.ai/) 123 | 124 | ## Misc tools and models 125 | 126 | - [Daniel Shiffman's Coding Train: The bestest creative coding videos](https://thecodingtrain.com/) 127 | - [Peter Baylies StyleGAN 2, trained on WikiArt images](https://github.com/pbaylies/stylegan2) 128 | - [Google Colab of this ^](https://colab.research.google.com/drive/1s7HPdmdOjBhvj1vhz9zP2d4rn_GhdoZR) 129 | - [Machine Learning Visual Art Colabs](https://github.com/dvschultz/ml-art-colabs) 130 | - [How to find new machine learning models and test them in Colab](https://www.youtube.com/watch?v=Ylb5pjCs1XU) 131 | - ["Entar" Colab pipeline for re-masteing, coloring old movies + Ken Burns effect / Denis Malimonov 132 | @tg-bomze](https://colab.research.google.com/github/tg-bomze/ENTAR/blob/master/ENTAR_Eng.ipynb) 133 | - [StyleGAN v2: notes on training and latent space exploration](https://medium.com/@5agado/stylegan-v2-notes-on-training-and-latent-space-exploration-e51cf96584b3) 134 | - [Train StyleGAN2 in Colab, also in arbitary image sizes](https://github.com/skyflynil/stylegan2) 135 | - [Runway Processing (not p5!) Library](https://github.com/runwayml/processing-library) 136 | - [A curated list of synthetic content generators](https://github.com/paubric/thisrepositorydoesnotexist) 137 | - [A collection of StyleGAN pretrained models](https://github.com/justinpinkney/awesome-pretrained-stylegan) 138 | - [StyleGAN encoder / Peter Baylies](https://github.com/pbaylies/stylegan-encoder) 139 | - [StyleGAN explorer, works in colab + UI](https://github.com/gpt2ent/stylegan-explorer) 140 | 141 | --------------------------------------------------------------------------------