Before A Computer Can Draw, It Must First Learn To See

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Derrall Heath and Dan Ventura: Before A Computer Can Draw, It Must First Learn To See. In: Computational Creativity 2016 ICCC 2016, 172-179



Most computationally creative systems lack adequate means of perceptually evaluating the artifacts they produce and are therefore not fully grounded in real world understanding. We argue that perceptually grounding such systems will increase their creative potential. Having adequate perceptual abilities can enable computational systems to be more autonomous, learn better internal models, evaluate their own artifacts, and create artifacts with intention. We draw from the fields of cognitive psychology, neuroscience, and art history to gain insights into the role that perception plays in the creative process. We use examples and methods from deep learning on the task of image generation and pareidolia to show the creative potential of systems with advanced perceptual abilities. We also discuss several issues and philosophical questions related to perception and creativity.

Extended Abstract


 author = {Derrall Heath and Dan Ventura},
 title = {Before A Computer Can Draw, It Must First Learn To See},
 booktitle = {Proceedings of the Seventh International Conference on Computational Creativity},
 series = {ICCC2016},
 year = {2016},
 month = {Jun-July},
 location = {Paris, France},
 pages = {172-179},
 url = {,_It_Must_First_Learn_To_See },
 publisher = {Sony CSL Paris},

Used References

Allen, C. B.; Celikel, T.; and Feldman, D. E. 2003. Long-term depression induced by sensory deprivation during cortical map plasticity in vivo. Nature Neuroscience 6(3):291–299.

Amedi, A.; Merabet, L. B.; Camprodon, J.; Bermpohl, F.; Fox, S.; Ronen, I.; Kim, D.-S.; and Pascual-Leone, A. 2008. Neural and behavioral correlates of drawing in an early blind painter: a case study. Brain Research 1242:252–262.

Bach-y-Rita, P., and Kercel, S. W. 2003. Sensory substitution and the human–machine interface. Trends in Cognitive Sciences 7(12):541–546.

Barsalou, L.W. 1999. Perceptual symbol systems. Behavioral and Brain Sciences 22(04):637–660.

Beaney, M. 2005. Imagination and Creativity. Open University Milton Keynes, UK.

Berns, G. 2008. Iconoclast: A neuroscientist reveals how to think differently. Harvard Business Press.

Chatterjee, A. 2004. The neuropsychology of visual artistic production. Neuropsychologia 42(11):1568–1583.

Colton, S.; Halskov, J.; Ventura, D.; Gouldstone, I.; Cook, M.; Blanca, P.; et al. 2015. The Painting Fool sees! new projects with the automated painter. In Proceedings of the 6th International Conference on Computational Creativity, 189–196.

Colton, S.; Goodwin, J.; and Veale, T. 2012. Full face poetry generation. In Proceedings of the Third International Conference on Computational Creativity, 95–102.

Colton, S. 2008. Creativity versus the perception of creativity in computational systems. Creative Intelligent Systems: Papers from the AAAI Spring Symposium 14–20.

Cope, D. 1996. Experiments in musical intelligence, volume 12. AR editions Madison, WI.

Cs´ıkzentmih´alyi, M., and Robinson, R. E. 1990. The Art of Seeing. The J. Paul Getty Trust Office of Publications.

Denton, E. L.; Chintala, S.; Fergus, R.; et al. 2015. Deep generative image models using a Laplacian pyramid of adversarial networks. In Advances in Neural Information Processing Systems, 1486–1494.

DiPaola, S., and Gabora, L. 2009. Incorporating characteristics of human creativity into an evolutionary art algorithm. Genetic Programming and Evolvable Machines 10(2):97–110.

Ebcio˘glu, K. 1988. An expert system for harmonizing four-part chorales. Computer Music Journal 12(3):43–51.

Edwards, B. 1989. Drawing on the Right Side of the Brain. New York: Tarcher.

Ellis, H. D., and Lewis, M. B. 2001. Capgras delusion: a window on face recognition. Trends in Cognitive Sciences 5(4):149–156.

Farabet, C.; Couprie, C.; Najman, L.; and LeCun, Y. 2013. Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(8):1915–1929. Farah, M. J. 2004. Visual Agnosia. MIT press.

Flowers, J. H., and Garbin, C. P. 1989. Creativity and perception. In Handbook of Creativity. Springer. 147–162.

Gaut, B. 2003. Creativity and imagination. The Creation of Art 148–173.

Grassian, S., and Friedman, N. 1986. Effects of sensory deprivation in psychiatric seclusion and solitary confinement. International Journal of Law and Psychiatry 8(1):49–65.

Gregor, K.; Danihelka, I.; Graves, A.; and Wierstra, D. 2015. DRAW: A recurrent neural network for image generation. In Proceedings of the 32nd International Conference on Machine Learning, 1462–1471.

Hamming, R. W. 1980. The unreasonable effectiveness of mathematics. American Mathematical Monthly 87:81–90.

Hawkins, J., and Blakeslee, S. 2007. On intelligence. Macmillan. Heath, D., and Ventura, D. 2016. Creating images by learning image semantics using vector space models. In Proceedings of The Thirtieth AAAI Conference on Artificial Intelligence.

Heath, D.; Dennis, A.; and Ventura, D. 2015. Imagining imagination: A computational framework using associative memory models and vector space models. In Proceedings of the 6th International Conference on Computational Creativity, 244–251.

Hoffman, D. D. 2000. Visual Intelligence: How We Create What We See. W.W. Norton.

Hong, K.; Chalup, S. K.; King, R.; Ostwald, M. J.; et al. 2013. Scene perception using pareidolia of faces and expressions of emotion. In IEEE Symposium on Computational Intelligence for Creativity and Affective Computing, 79–86.

Jia, Y.; Shelhamer, E.; Donahue, J.; Karayev, S.; Long, J.; Girshick, R.; Guadarrama, S.; and Darrell, T. 2014. Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the ACM International Conference on Multimedia, 675–678.

Kamel, H. M., and Landay, J. A. 2000. A study of blind drawing practice: creating graphical information without the visual channel. In Proceedings of the Fourth International ACM Conference on Assistive Technologies, 34–41.

Kennedy, J. M. 1993. Drawing & the Blind: Pictures to Touch. Yale University Press.

Lafer-Sousa, R.; Hermann, K. L.; and Conway, B. R. 2015. Striking individual differences in color perception uncovered by ‘the dress’ photograph. Current Biology 25(13):R545–R546.

Leon A. Gatys, A. S. E., and Bethge, M. 2015. A neural algorithm of artistic style. Computing Research Repository.

Levi, G., and Hassner, T. 2015. Age and gender classification using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 34–42.

Machado, P.; Romero, J.; and Manaris, B. 2007. Experiments in computational aesthetics: An iterative approach to stylistic change in evolutionary art. In Romero, J., and Machado, P., eds., The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music. Berlin: Springer. 381–415.

Marmor, M. F., and Ravin, J. 2009. The Artist’s Eyes. Harry N Abrams Incorporated.

Melo, A. F., and Wiggins, G. 2003. A connectionist approach to driving chord progressions using tension. In Proceedings of the AISB Symposium on Creativity in Arts and Science.

Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. A.; Veness, J.; Bellemare, M. G.; Graves, A.; Riedmiller, M.; Fidjeland, A. K.; Ostrovski, G.; et al. 2015. Human-level control through deep reinforcement learning. Nature 518(7540):529–533.

Morris, R. G.; Burton, S. H.; Bodily, P. M.; and Ventura, D. 2012. Soup over bean of pure joy: Culinary ruminations of an artificial chef. In Proceedings of the 3rd International Conference on Computational Creativity, 119–125.

Netzer, Y.; Gabay, D.; Goldberg, Y.; and Elhadad, M. 2009. Gaiku: Generating haiku with word associations norms. In Proceedings of the Workshop on Computational Approaches to Linguistic Creativity, 32–39.

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

Pachet, F., and Roy, P. 2011. Markov constraints: Steerable generation of Markov sequences. Constraints 16(2):148–172.

Parkhi, O. M.; Vedaldi, A.; and Zisserman, A. 2015. Deep face recognition. British Machine Vision 1(3):6.

Peterson, E. M. 2006. Creativity in music listening. Arts Education Policy Review 107(3):15–21.

Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; Berg, A. C.; and Fei-Fei, L. 2015. ImageNet large scale visual recognition challenge. International Journal of Computer Vision 115(3):211– 252.

Sacks, O. 1995. An Anthropologist on Mars. New York: Knopf. Searle, J. R. 1980. Minds, brains, and programs. Behavioral and Brain Sciences 3(03):417–424.

Simonyan, K.; Vedaldi, A.; and Zisserman, A. 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034.

Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; and Rabinovich, A. 2015. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–9.

Varshney, L. R.; Pinel, F.; Varshney, K. R.; Sch¨orgendorfer, A.; and Chee, Y.-M. 2013. Cognition as a part of computational creativity. In Procceedings of the 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, 36–43.

Weiskrantz, L. 1996. Blindsight revisited. Current Opinion In Neurobiology 6(2):215–220.

Zwaan, R. A., and Kaschak, M. P. 2008. Language in the brain, body, and world. The Cambridge Handbook of Situated Cognition 368–381.


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