Here’s how I would do art with machine learning if I had to

I’ve a weakness for ideas that give me plausible deniability for making generative art while doing my maths homework.

Quasimondo: so do you

This page is more chaotic than the already-chaotic median, sorry. Good luck making sense of it.

See also analysis/resynthesis.

See gesture recognition. Oh and also google’s AMI channel, and ml4artists, which has some sweet machine learning for artists topic guides.

Many neural networks, are generative in the sense that even if you train ’em to classify things, they can also predict new members of the class. e.g. run the model forwards, it recognizes melodies; run it “backwards”, it composes melodies. Or rather, you maybe trained them to generate examples in the course of training them to detect examples.

There are many definitional and practical wrinkles here, and this quality is not unique to artificial neural networks, but it is a great convenience, and the gods of machine learning have blessed us with much infrastructure to exploit this feature, because it is very close to actual profitable algorithms. Upshot: There is now a lot of computation and grad student labour directed at producing neural networks which as a byproduct can produce faces, chairs, film dialogue, symphonies and so on.

There are NeurIPS streams about this now.


Some as-yet-unfiled neural-artwork links I should think about.

Text synthesis

Visual synthesis

See those classic images from google’s tripped-out image recognition systems) or Gatys, Ecker and Bethge’s deep art Neural networks do a passable undergraduate Monet.

Here’s Frank Liu’s implementation of style transfer in pycaffe.

Alex Graves, Generating Sequences With Recurrent Neural Networks, generates handwriting. Relatedly, sketch-rnn is reaaaally cute.

Deep dreaming approaches are entertaining. (NSFW) Here’s a more pedestrian and slightly more informative version of that. has some lovely visual explanations of visual and other neural networks.

  • hardmaru presents an amazing introduction to running sophisticated neural networks in the browser, targeted at artists, which goes over the handwriting post in a non-technical way.

  • progressive_growing_of_gans is neat, generating infinite celebrities at high resolution. (Karras et al. 2017)


    a platform for creators of all kinds to use machine learning tools in intuitive ways without any coding experience. Find resources here to start creating with RunwayML quickly.

    In particular it plugs into Blender and Photoshop and allows you to use those programs as a UI for ML-backed algorithms. Nice.


Symbolic composition via scores/MIDI/etc

See ML for composition.

Audio synthesis

See analysis/resynthesis, voice fakes.

Boulanger-Lewandowski, Nicolas, Yoshua Bengio, and Pascal Vincent. 2012. “Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription.” In 29th International Conference on Machine Learning.

Bown, Oliver, and Sebastian Lexer. 2006. “Continuous-Time Recurrent Neural Networks for Generative and Interactive Musical Performance.” In Applications of Evolutionary Computing, edited by Franz Rothlauf, Jürgen Branke, Stefano Cagnoni, Ernesto Costa, Carlos Cotta, Rolf Drechsler, Evelyne Lutton, et al., 652–63. Lecture Notes in Computer Science 3907. Springer Berlin Heidelberg.

Briot, Jean-Pierre, and François Pachet. 2020. “Deep Learning for Music Generation: Challenges and Directions.” Neural Computing and Applications 32 (4): 981–93.

Champandard, Alex J. 2016. “Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks.” March 5, 2016.

Denton, Emily, Soumith Chintala, Arthur Szlam, and Rob Fergus. 2015. “Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks.” June 18, 2015.

Dieleman, Sander, and Benjamin Schrauwen. 2014. “End to End Learning for Music Audio.” In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6964–8. IEEE.

Dosovitskiy, Alexey, Jost Tobias Springenberg, Maxim Tatarchenko, and Thomas Brox. 2014. “Learning to Generate Chairs, Tables and Cars with Convolutional Networks.” November 21, 2014.

Dumoulin, Vincent, Jonathon Shlens, and Manjunath Kudlur. 2016. “A Learned Representation for Artistic Style.” October 24, 2016.

Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. 2015. “A Neural Algorithm of Artistic Style.” August 26, 2015.

Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. 2014. “Explaining and Harnessing Adversarial Examples.” December 19, 2014.

Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. “Generative Adversarial Nets.” In Advances in Neural Information Processing Systems 27, edited by Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, 2672–80. NIPS’14. Cambridge, MA, USA: Curran Associates, Inc.

Gregor, Karol, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, and Daan Wierstra. 2015. “DRAW: A Recurrent Neural Network for Image Generation.” February 16, 2015.

Gregor, Karol, and Yann LeCun. 2010. “Learning Fast Approximations of Sparse Coding.” In Proceedings of the 27th International Conference on Machine Learning (ICML-10), 399–406.

———. 2011. “Efficient Learning of Sparse Invariant Representations.” May 26, 2011.

Grosse, Roger, Ruslan R. Salakhutdinov, William T. Freeman, and Joshua B. Tenenbaum. 2012. “Exploiting Compositionality to Explore a Large Space of Model Structures.” In Proceedings of the Conference on Uncertainty in Artificial Intelligence.

He, Kun, Yan Wang, and John Hopcroft. 2016. “A Powerful Generative Model Using Random Weights for the Deep Image Representation.” In Advances in Neural Information Processing Systems.

Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. 2006. “Reducing the Dimensionality of Data with Neural Networks.” Science 313 (5786): 504–7.

Jetchev, Nikolay, Urs Bergmann, and Roland Vollgraf. 2016. “Texture Synthesis with Spatial Generative Adversarial Networks.” In Advances in Neural Information Processing Systems 29.

Jing, Yongcheng, Yezhou Yang, Zunlei Feng, Jingwen Ye, and Mingli Song. 2017. “Neural Style Transfer: A Review.” May 11, 2017.

Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. 2016. “Perceptual Losses for Real-Time Style Transfer and Super-Resolution.” March 26, 2016.

Karras, Tero, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2017. “Progressive Growing of GANs for Improved Quality, Stability, and Variation.” In Proceedings of ICLR.

Karras, Tero, Samuli Laine, and Timo Aila. 2018. “A Style-Based Generator Architecture for Generative Adversarial Networks.” December 12, 2018.

Larsen, Anders Boesen Lindbo, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther. 2015. “Autoencoding Beyond Pixels Using a Learned Similarity Metric.” December 31, 2015.

Lazaridou, Angeliki, Dat Tien Nguyen, Raffaella Bernardi, and Marco Baroni. 2015. “Unveiling the Dreams of Word Embeddings: Towards Language-Driven Image Generation.” June 10, 2015.

Li, Yanghao, Naiyan Wang, Jiaying Liu, and Xiaodi Hou. 2017. “Demystifying Neural Style Transfer.” In IJCAI.

Luo, Yi, Zhuo Chen, John R. Hershey, Jonathan Le Roux, and Nima Mesgarani. 2016. “Deep Clustering and Conventional Networks for Music Separation: Stronger Together.” November 18, 2016.

Malmi, Eric, Pyry Takala, Hannu Toivonen, Tapani Raiko, and Aristides Gionis. 2016. “DopeLearning: A Computational Approach to Rap Lyrics Generation.” 2016.

Mital, Parag K. 2017. “Time Domain Neural Audio Style Transfer.” November 29, 2017.

Mnih, Andriy, and Karol Gregor. 2014. “Neural Variational Inference and Learning in Belief Networks.” In Proceedings of the 31st International Conference on Machine Learning.

Olah, Chris, Alexander Mordvintsev, and Ludwig Schubert. 2017. “Feature Visualization.” Distill 2 (11): e7.

Oord, Aäron van den. 2016. “Wavenet: A Generative Model for Raw Audio.”

Oord, Aäron van den, Nal Kalchbrenner, and Koray Kavukcuoglu. 2016. “Pixel Recurrent Neural Networks.” January 25, 2016.

Oord, Aäron van den, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu. 2016. “Conditional Image Generation with PixelCNN Decoders.” June 16, 2016.

Sarroff, Andy M., and Michael Casey. 2014. “Musical Audio Synthesis Using Autoencoding Neural Nets.” In. Ann Arbor, MI: Michigan Publishing, University of Michigan Library.

Sigtia, Siddharth, Emmanouil Benetos, Nicolas Boulanger-Lewandowski, Tillman Weyde, Artur S. d’Avila Garcez, and Simon Dixon. 2015. “A Hybrid Recurrent Neural Network for Music Transcription.” In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2061–5. IEEE.

Smith, Evan C., and Michael S. Lewicki. 2006. “Efficient Auditory Coding.” Nature 439 (7079): 978–82.

Sturm, Bob L., Oded Ben-Tal, Úna Monaghan, Nick Collins, Dorien Herremans, Elaine Chew, Gaëtan Hadjeres, Emmanuel Deruty, and François Pachet. 2018. “Machine Learning Research That Matters for Music Creation: A Case Study.” Journal of New Music Research 0 (0): 1–20.

Sun, Zheng, Jiaqi Liu, Zewang Zhang, Jingwen Chen, Zhao Huo, Ching Hua Lee, and Xiao Zhang. 2016. “Composing Music with Grammar Argumented Neural Networks and Note-Level Encoding.” November 16, 2016.

Theis, Lucas, and Matthias Bethge. 2015. “Generative Image Modeling Using Spatial LSTMs.” June 10, 2015.

Ulyanov, Dmitry, Andrea Vedaldi, and Victor Lempitsky. 2016. “Instance Normalization: The Missing Ingredient for Fast Stylization.” July 27, 2016.

———. 2017. “Improved Texture Networks: Maximizing Quality and Diversity in Feed-Forward Stylization and Texture Synthesis.” January 9, 2017.

Walder, Christian. 2016a. “Modelling Symbolic Music: Beyond the Piano Roll.” June 4, 2016.

———. 2016b. “Symbolic Music Data Version 1.0.” June 8, 2016.

Wu, Qi, Chunhua Shen, Anton van den Hengel, Lingqiao Liu, and Anthony Dick. 2015. “What Value High Level Concepts in Vision to Language Problems?” June 3, 2015.

Wyse, L. 2017. “Audio Spectrogram Representations for Processing with Convolutional Neural Networks.” In Proceedings of the First International Conference on Deep Learning and Music, Anchorage, US, May, 2017 (arXiv:1706.08675v1 [cs.NE]).

Yu, D., and L. Deng. 2011. “Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP].” IEEE Signal Processing Magazine 28 (1): 145–54.

Yu, Haizi, and Lav R. Varshney. 2017. “Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music.” In Proceedings of International Conference on Learning Representations (ICLR) 2017.

Zhu, Jun-Yan, Philipp Krähenbühl, Eli Shechtman, and Alexei A. Efros. 2016. “Generative Visual Manipulation on the Natural Image Manifold.” In Proceedings of European Conference on Computer Vision.

Zukowski, Zack, and Cj Carr. 2017. “Generating Black Metal and Math Rock: Beyond Bach, Beethoven, and Beatles.” In 31st Conference on Neural Information Processing Systems (NIPS 2017).