First Edition

All available online-content for the first edition AI & Arts can be found below.


Article – Alan Turing

Click here to read the article’s long version.


History AI & Art

DeepBeat website

References

[1] Malmi, E., Takala, P., Toivonen, H., Raiko, T., & Gionis, A. (2016, August). Dopelearning: A computational approach to rap lyrics generation. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 195-204). ACM.

[2] Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576.

[3] Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2018). Self-attention generative adversarial networks.arXiv preprint arXiv:1805.08318.


To AI or not to AI

Github code for generating rhymes


Augmenting Art

Related article from de Gelderlander (in dutch).

While there is no GitHub page containing the code for either of the mentioned apps, Loes is happy to help should any readers want more information. You can contact her via mail at l.vanbemmel@student.ru.nl


Measuring Subjective Phenomena

Spoiler title
Artists:
Researchers:


Music Composition with AI

References

[1] Todd, P. (1989). A Connectionist Approach to Algorithmic Composition. Computer Music Journal, 13(4), 27-43. doi:10.2307/3679551

[2] Mozer, M. C. (1994). Neural network music composition by prediction: Exploring the benefits of psychoacoustic constraints and multi-scale processing. Connection Science6(2-3), 247-280.

[3] Eck, D., & Schmidhuber, J. (2002, September). Finding temporal structure in music: Blues improvisation with LSTM recurrent networks. In Proceedings of the 12th IEEE workshop on neural networks for signal processing (pp. 747-756). IEEE.

[4] Bickerman, G., Bosley, S., Swire, P., & Keller, R. M. (2010, January). Learning to Create Jazz Melodies Using Deep Belief Nets. In ICCC (pp. 228-237).

More links


Cyber Couture

Further reading

Biography

Anneke Smelik is Katrien van Munster professor of Visual Culture at the Radboud University Nijmegen. She has published widely in the field of fashion, cinema, popular culture and cultural memory. Her latest books are Delft Blue to Denim Blue. Contemporary Dutch Fashion and Thinking Through Fashion. A Guide to Key Theorists. She wrote a book in Dutch on cyborgs: Ik cyborg. De mens-machine in populaire cultuur.


Battle of Neural Networks

References

[1] Censorship of images in the Soviet Union (Wikipedia)

[2] Where does the term  fake news come from | Fallon, C. (2017) (HuffPost)

[3] Why humans run the world | Harari, Y.N (2015)

[4] Generative adversarial network (Wikipedia)

[5] A Beginner’s Guide to Generative Adversarial Networks (GANs) | Nicholson, C. (Skymind AI wiki)

[6] Generative Adversarial Networks (GANs) for Beginners: Generating Images of Distracted Drivers | Monge, Z. (2019) (Towards Data Science)

[7] Generative Adversarial Networks recover features in astrophysical images of galaxies | Fowler, L. et al. (2017) (space.ml Galaxy GAN)

[8] GILT: Generating Images from Long Text | Bar El, O. et al. (2019)

[9] Sketch-Based 3D Exploration with Stacked Generative Adversarial Networks (github)

[10] Exploring galaxy evolution with generative models | Schawinski, K. et al. (2018)

Fun stuff

Want to play with GANs? Here’s a great link with various types of GANs, ready for you to use:
https://github.com/eriklindernoren/Keras-GAN


Generating images with GANs

References

[1] Original paper
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D.,
Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial
nets. In Advances in Neural Information Processing Systems (NIPS) 2014,
pages 2672?2680.

[2] DCGAN
Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised
representation learning with deep convolutional generative adversarial
networks. arXiv preprint arXiv:1511.06434.

[3] BigGAN
Brock, A., Donahue, J., & Simonyan, K. (2018). Large scale gan training
for high fidelity natural image synthesis. arXiv preprint
arXiv:1809.11096.

[4] We have used this particular DCGAN for this paper:
Seeliger, K., Güçlü, U., Ambrogioni, L.,Güçlütürk, Y., & van Gerven, M.
A. (2018). Generative adversarial networks for reconstructing natural
images from brain activity. NeuroImage, 181, 775-785.


Turning sketches into photos with AI

Turning sketches into photos with AI

Recommendations to read, watch, do


 

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