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

Visual synthesis
Text synthesis
Music
- Symbolic composition via scores/MIDI/etc
- Audio synthesis
References

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

NB I have recently tidied this page up but the content is not fresh; there is too much happening in this field to document.

Quasimondo: so do you.

This page is more chaotic than the already-chaotic median, sorry. Good luck making sense of it. The problem is that this notebook is in the anti-sweet spot of “stuff I know too much about to need notes but not working on enough to promote”.

Some neural networks are generative, in the sense that 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, and this ability 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 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.

Perhaps other people will be more across this?

Oh and also google’s AMI channel, and ml4artists, which publishes sweet machine learning for artists topic guides.

There are NeurIPS streams about this now.

Visual synthesis

There is a lot going on, which I should triage. Important example: Maybe I should also do generative art with neural networks.

AI image editors

See image editing with aI.

Style transfer and deep dreaming

You can do style transfer a number of ways, including NN inversion and GANs.

See those classic images from google’s tripped-out image recognition systems). Here’s a good explanation of what is going on.

deep art:
Our mission is to provide a novel artistic painting tool that allows everyone to create and share artistic pictures with just a few clicks. All you need to do is upload a photo and choose your favorite style. Our servers will then render your artwork for you.
For example, OPEN_NSFW was my favourite (NSFW).
Differentiable Image Parameterizations looks at style transfer with respect to different decompositions of the image surface. (There is stuff to follow up about checkerboard artefacts in NNs which I suspect is generally important.)
Self-Organising Textures stitches these two together, using a VGG discriminator as a loss function for training textures.
Deep dream generator does the classic deep-dreaming style perturbations
fast style transfer.

GANs

Clever uses of GANs that are not style transfer.

Adversarial generation is a cool hack is a cool hack if you hate boring stuff like labelling data sets e.g. chair generation
big-sleep: A simple command line tool for text to image generation, using OpenAI’s CLIP and a BigGAN (HT Ben Leighton for pointing this out)
iGAN, iGAN: Interactive Image Generation via Generative Adversarial Networks
interpolating style transfer.
progressive_growing_of_gans is neat, generating infinite celebrities at high resolution. (Karras et al. 2017)

Incoming

Neural networks do a passable undergraduate Monet.
messing with copyright lawyers’ minds by compressing films to vectors (More technical version)
hardmaru explains Compositional pattern-producing network (Stanley 2007) These are apparently a classic type of implicit rep NNs. Probably one of the things that Jonathan McCabe has been producing for years.
neurogram is a compact semi-untrained neural network image synthesis-in-the-browser
hardmaru introduces artists to sophisticated neural networks in the browser.
Growing Neural Cellular Automata looks at differentiable automata on a grid for prettiness.
Finding Mona Lisa in the Game of Life
artbreeder is quixotically old-school.
Alex Graves, Generating Sequences With Recurrent Neural Networks, generates handwriting.
Relatedly, sketch-rnn is reaaaally cute.
Autoencoding beyond pixels using a learned similarity metric (Larsen et al. 2015). code

Text synthesis

At one point, Ross Gibson’s Adventures in narrated reality was a state-of-the-art text generation using RNNs. He even made a movie of a script generated that way. But now, massive transformer models have left it behind technologically, ff not humorously.

Music

Symbolic composition via scores/MIDI/etc

See ML for composition.

Audio synthesis

See analysis/resynthesis, voice fakes.

References

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.

Carlier, Alexandre, Martin Danelljan, Alexandre Alahi, and Radu Timofte. 2020. “DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation.” In.

Champandard, Alex J. 2016. “Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks.” arXiv:1603.01768 [Cs], March.

Denton, Emily, Soumith Chintala, Arthur Szlam, and Rob Fergus. 2015. “Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks.” arXiv:1506.05751 [Cs], June.

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), 6964–68. IEEE.

Dosovitskiy, Alexey, Jost Tobias Springenberg, Maxim Tatarchenko, and Thomas Brox. 2014. “Learning to Generate Chairs, Tables and Cars with Convolutional Networks.” arXiv:1411.5928 [Cs], November.

Dumoulin, Vincent, Jonathon Shlens, and Manjunath Kudlur. 2016. “A Learned Representation For Artistic Style.” arXiv:1610.07629 [Cs], October.

Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. 2015. “A Neural Algorithm of Artistic Style.” arXiv:1508.06576 [Cs, q-Bio], August.

Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. 2014. “Explaining and Harnessing Adversarial Examples.” arXiv:1412.6572 [Cs, Stat], December.

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.” arXiv:1502.04623 [Cs], February.

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.” arXiv:1105.5307 [Cs], May.

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.” arXiv:1705.04058 [Cs], May.

Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. 2016. “Perceptual Losses for Real-Time Style Transfer and Super-Resolution.” arXiv:1603.08155 [Cs], March.

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.” arXiv:1812.04948 [Cs, Stat], December.

Larsen, Anders Boesen Lindbo, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther. 2015. “Autoencoding Beyond Pixels Using a Learned Similarity Metric.” arXiv:1512.09300 [Cs, Stat], December.

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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.” arXiv:1611.06265 [Cs, Stat], November.

Malmi, Eric, Pyry Takala, Hannu Toivonen, Tapani Raiko, and Aristides Gionis. 2016. “DopeLearning: A Computational Approach to Rap Lyrics Generation.” arXiv:1505.04771 [Cs], 195–204.

Mital, Parag K. 2017. “Time Domain Neural Audio Style Transfer.” arXiv:1711.11160 [Cs], November.

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