Digits that are not: Generating new types through deep neural nets

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


Kazakci, Mehdi Cherti and Balazs Kegl: Digits that are not: Generating new types through deep neural nets. In: Computational Creativity 2016 ICCC 2016, 188-195



For an artificial creative agent, an essential driver of the search for novelty is a value function which is often provided by the system designer or users. We argue that an important barrier for progress in creativity research is the inability of these systems to develop their own notion of value for novelty. We propose a notion of knowledge-driven creativity that circumvent the need for an externally imposed value function, allowing the system to explore based on what it has learned from a set of referential objects. The concept is illustrated by a specific knowledge model provided by a deep generative autoencoder. Using the described system, we train a knowledge model on a set of digit images and we use the same model to build coherent sets of new digits that do not belong to known digit types.

Extended Abstract


 author = {Kazakci, Mehdi Cherti and Balazs Kegl},
 title = {Digits that are not: Generating new types through deep neural nets},
 booktitle = {Proceedings of the Seventh International Conference on Computational Creativity},
 series = {ICCC2016},
 year = {2016},
 month = {Jun-July},
 location = {Paris, France},
 pages = {188-195},
 url = {http://www.computationalcreativity.net/iccc2016/wp-content/uploads/2016/01/Digits-that-are-not.pdf http://de.evo-art.org/index.php?title=Digits_that_are_not:_Generating_new_types_through_deep_neural_nets },
 publisher = {Sony CSL Paris},

Used References

[Baldi and Hornik 1989] Baldi, P., and Hornik, K. 1989. Neural networks and principal component analysis: Learning from examples without local minima. Neural networks 2(1):53–58.

[Beaucousin et al. 2011] Beaucousin, V.; Cassotti, M.; Simon, G.; Pineau, A.; Kostova, M.; Houd´e, O.; and Poirel, N. 2011. Erp evidence of a meaningfulness impact on visual global/local processing: when meaning captures attention. Neuropsychologia 49(5):1258–1266.

[Bengio et al. 2013] Bengio, Y.; Yao, L.; Alain, G.; and Vincent, P. 2013. Generalized denoising auto-encoders as generative models. In Advances in Neural Information Processing Systems, 899–907.

[Boden and Edmonds 2009] Boden, M. A., and Edmonds, E. A. 2009. What is generative art? Digital Creativity 20(1-2):21–46.

[Breton 1932] Breton, A. 1932. Marcel duchamp : The bride stripped bare by her own bachelors. This Quarter Surrealist Number 5(1).

[Clune and Lipson 2011] Clune, J., and Lipson, H. 2011. Evolving three-dimensional objects with a generative encoding inspired by developmental biology. In Proceedings of the European Conference on Artificial Life, 144–148.

[Diedrich et al. 2015] Diedrich, J.; Benedek, M.; Jauk, E.; and Neubauer, A. C. 2015. Are creative ideas novel and useful? Psychology of Aesthetics, Creativity, and the Arts 9(1):35.

[Edmonds 1969] Edmonds, E. 1969. Independence of rose’s axioms for m-valued implication. The Journal of Symbolic Logic 34(02):283–284.

[Galanter 2012] Galanter, P. 2012. Generative art after computers,[ in:] generative art–proceedings ga2012 xv generative art conference, red. soddu c.

[Gatys, Ecker, and Bethge 2015] Gatys, L. A.; Ecker, A. S.; and Bethge, M. 2015. A neural algorithm of artistic style.

[Goodfellow et al. 2014] 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, 2672–2680.

[Hatchuel and Weil 2009] Hatchuel, A., and Weil, B. 2009. Ck design theory: an advanced formulation. Research in engineering design 19(4):181–192.

[Hinton, Osindero, and Teh 2006] Hinton, G. E.; Osindero, S.; and Teh, Y.-W. 2006. A fast learning algorithm for deep belief nets. Neural computation 18(7):1527–1554.

[Jennings 2010] Jennings, K. E. 2010. Developing creativity: Artificial barriers in artificial intelligence. Minds and Machines 20(4):489–501.

[Kamyshanska and Memisevic 2013] Kamyshanska, H., and Memisevic, R. 2013. On autoencoder scoring. In Proceedings of the 30th International Conference on Machine Learning (ICML-13), 720–728.

[Kazakci et al. 2010] Kazakci, A.; Hatchuel, A.; Le Masson, P.; Weil, B.; et al. 2010. Simulation of design reasoning based on ck theory: a model and an example application. In DS 60: Proceedings of DESIGN 2010, the 11th International Design Conference, Dubrovnik, Croatia.

[Kazakc¸ı 2014] Kazakc¸ı, A. 2014. Conceptive artificial intelligence: Insights from design theory. In International Design Conference DESIGN2014, 1–16.

[Kramer 1991] Kramer, M. A. 1991. Nonlinear principal component analysis using autoassociative neural networks. AIChE journal 37(2):233–243.

[Lecun and Cortes 2012] Lecun, Y., and Cortes, C. 2012.

[LeCun, Bengio, and Hinton 2015] LeCun, Y.; Bengio, Y.; and Hinton, G. 2015. Deep learning. Nature 521(7553):436–444.

[Lehman and Stanley 2011] Lehman, J., and Stanley, K. O. 2011. Novelty search and the problem with objectives. In Genetic Programming Theory and Practice IX, 37–56. Springer.

[Machado, Romero, and Manaris 2008] Machado, P.; Romero, J.; and Manaris, B. 2008. Experiments in computational aesthetics. In The art of artificial evolution. Springer. 381–415.

[Makhzani and Frey 2015] Makhzani, A., and Frey, B. J. 2015. Winner-take-all autoencoders. In Advances in Neural Information Processing Systems, 2773–2781.

[McCormack et al. 2014] McCormack, J.; Bown, O.; Dorin, A.; McCabe, J.; Monro, G.; and Whitelaw, M. 2014. Ten questions concerning generative computer art. Leonardo 47(2):135–141.

[McCormack 2013] McCormack, J. 2013. Aesthetics, art, evolution. Springer.

[Mordvintsev, Olah, and Tyka 2015] Mordvintsev, A.; Olah, C.; and Tyka, M. 2015. Inceptionism: Going deeper into neural networks. Google Research Blog. Retrieved June 20.

[Mouret and Doncieux 2012] Mouret, J.-B., and Doncieux, S. 2012. Encouraging behavioral diversity in evolutionary robotics: An empirical study. Evolutionary computation 20(1):91–133.

[Nees 1969] Nees, G. 1969. Generative Computergraphik. Siemens AG.

[Nguyen, Yosinski, and Clune 2015] Nguyen, A. M.; Yosinski, J.; and Clune, J. 2015. Innovation engines: Automated creativity and improved stochastic optimization via deep learning. In Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, 959–966. ACM.

[Parikka 2008] Parikka, J. 2008. Leonardo book review: The art of artificial evolution: A handbook on evolutionary art and music.

[Rumelhart, Hinton, and Williams 1986] Rumelhart, D. E.; Hinton, G. E.; andWilliams, R. J. 1986. Learning representations by backpropagating errors. NATURE 323:9.

[Runco and Jaeger 2012] Runco, M. A., and Jaeger, G. J. 2012. The standard definition of creativity. Creativity Research Journal 24(1):92–96.

[Salakhutdinov and Hinton 2009] Salakhutdinov, R., and Hinton, G. E. 2009. Deep boltzmann machines. In International Conference on Artificial Intelligence and Statistics, 448–455.

[Secretan et al. 2008] Secretan, J.; Beato, N.; D Ambrosio, D. B.; Rodriguez, A.; Campbell, A.; and Stanley, K. O. 2008. Picbreeder: evolving pictures collaboratively online. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1759–1768. ACM.

[Sims 1991] Sims, K. 1991. Artificial evolution for computer graphics, volume 25. ACM.

[Takagi 2001] Takagi, H. 2001. Interactive evolutionary computation: Fusion of the capabilities of ec optimization and human evaluation. Proceedings of the IEEE 89(9):1275–1296.

[Thaler 1998] Thaler, S. L. 1998. The emerging intelligence and its critical look at us. Journal of Near-Death Studies 17(1):21–29.

[Theis, Oord, and Bethge 2015] Theis, L.; Oord, A. v. d.; and Bethge, M. 2015. A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844.

[Todd and Latham 1991] Todd, S., and Latham, W. 1991. Mutator: a subjective human interface for evolution of computer sculptures. IBM United Kingdom Scientific Centre.

[Todd 1989] Todd, P. M. 1989. A connectionist approach to algorithmic composition. Computer Music Journal 13(4):27–43.

[Todd 1992] Todd, P. M. 1992. A connectionist system for exploring melody space. In Proceedings of the International Computer Music Conference, 65–65. International Computer Association.

[van der Maaten and Hinton 2008] van der Maaten, L., and Hinton, G. 2008. Visualizing data using t-SNE. The Journal of Machine Learning Research 9(2579-2605):85.

[Von Hippel 1986] Von Hippel, E. 1986. Lead users: a source of novel product concepts. Management science 32(7):791–805.

[Wiggins 2006] Wiggins, G. A. 2006. A preliminary framework for description, analysis and comparison of creative systems. Knowledge-Based Systems 19(7):449–458.


Full Text


intern file

Sonstige Links