Deep Convolutional Networks as Models of Generalization and Blending within Visual Creativity
Inhaltsverzeichnis
Reference
Graeme McCaig, Steve Dipaola and Liane Gabora: Deep Convolutional Networks as Models of Generalization and Blending within Visual Creativity. In: Computational Creativity 2016 ICCC 2016, 156-163
DOI
Abstract
We examine two recent artificial intelligence (AI) based deep learning algorithms for visual blending in convolutional neural networks (Mordvintsev et al. 2015, Gatys et al. 2015). To investigate the potential value of these algorithms as tools for computational creativity research, we explain and schematize the essential aspects of the algorithms’ operation and give visual examples of their output. We discuss the relationship of the two algorithms to human cognitive science theories of creativity such as conceptual blending theory and honing theory, and characterize the algorithms with respect to generation of novelty and aesthetic quality.
Extended Abstract
Bibtex
@inproceedings{
author = {Graeme McCaig, Steve Dipaola and Liane Gabora},
title = {Deep Convolutional Networks as Models of Generalization and Blending within Visual Creativity},
booktitle = {Proceedings of the Seventh International Conference on Computational Creativity},
series = {ICCC2016},
year = {2016},
month = {Jun-July},
location = {Paris, France},
pages = {156-163},
url = {http://www.computationalcreativity.net/iccc2016/wp-content/uploads/2016/01/Deep-Convolutional-Networks-as-Models-of-Generalization-and-Blending.pdf http://de.evo-art.org/index.php?title=Deep_Convolutional_Networks_as_Models_of_Generalization_and_Blending_within_Visual_Creativity },
publisher = {Sony CSL Paris},
}
Used References
Aerts, D., Broekaert, J., Gabora, L. & Sozzo, S. 2016. Generalizing prototype theory: A formal quantum framework. Frontiers in Psychology 7.
Augello, A., Infantino, I., Pilato, G., Rizzo, R., & Vella, F. 2013. Introducing a creative process on a cognitive architecture. Bio. Inspired Cognitive Architectures 6:131–139.
Bengio, Y., Courville, A., & Vincent, P. 2013. Representation learning: A review and new perspectives. IEEE Trans. on Pattern Analysis and Machine Intell. 35(8):1798-1828.
Besold, T. R., & Plaza, E. 2015. Generalize and blend: Concept blending based on generalization, analogy, and amalgams. In Proceedings of the Sixth International Conference on Computational Creativity, 150-157.
Boden, M. A. 2004. The Creative Mind: Myths and Mechanisms. Psychology Press.
Confalonieri, R., Corneli, J., Pease, A., Plaza, E. and Schorlemmer, M. 2015. Using argumentation to evaluate concept blends in combinatorial creativity. In Proc. of the Sixth Int. Conf. on Computational Creativity, 174-181.
Denton, E. L., Chintala, S., & Fergus, R. 2015. Deep generative image models using a Laplacian pyramid of adversarial networks. In Advances in Neural Information Processing Systems, 1486-1494.
DiCarlo, J., Zoccolan, D., & Rust, N. 2012. How does the brain solve visual object recognition? Neuron 73(3):415– 434.
DiPaola, S. 2009. Exploring a parameterised portrait painting space. Int. Journal of Arts & Technology 2(1):82–93.
DiPaola, S., & Gabora, L. 2009. Incorporating characteristics of human creativity into an evolutionary art algorithm. Genetic Programming & Evolvable Machines 10:97–110.
Fauconnier, G., & Turner, M. 1998. Conceptual integration networks. Cognitive Science 22(2):133-187.
Gabora, L. 2005. Creative thought as a non-Darwinian evolutionary process. J. Creative Behav. 39(4):262-283.
Gabora, L., & Ranjan, A. 2013. How insight emerges in distributed, content-addressable memory. In Neuroscience of Creativity. Cambridge, MA: MIT Press. 19-43.
Gatys, L. A., Ecker, A. S., & Bethge, M. 2015. A neural algorithm of artistic style. arXiv:1508.06576.
Heath, D., Dennis, A. & Ventura, D., 2015. Imagining imagination: A computational framework using associative memory models and vector space models. In Proc. of the Sixth Int. Conf. on Computational Creativity, 244-251.
Jennings, K. E. 2010. Developing creativity: Artificial barriers in artificial intelligence. Minds and Machines 20(4): 489-501.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., ... & Darrell, T. 2014. Caffe: Convolutional architecture for fast feature embedding. In Proc. ACM International Conf. on Multimedia, 675-678.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, 1097-1105.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11):2278–2324.
Martins, P., Urbancic, T., Pollak, S., Lavrac, N., & Cardoso, A. 2015. The Good, the Bad, and the AHA! Blends. In Proc. Sixth Int. Conf. on Comp. Creativity, 166-173.
Mordvintsev, A., Olah, C., Tyka, M. 2015. Inceptionism: Going deeper into neural networks. URL: googleresearch. blogspot.com/2015/06/inceptionism-going-deeper-intoneural. html
Pereira, F. C., & Cardoso, A. 2002. Conceptual blending and the quest for the holy creative process. In Proc. ASIB’02 Symposium for Creativity in Arts and Science.
Ramachandran, V. S., & Hirstein, W. 1999. The science of art: A neurological theory of aesthetic experience. Journal of Consciousness Studies 6(6-7):15-51.
Richardson, A. 2015. Imagination: Literary and cognitive intersections. In Oxford Handbook of Cognitive Literary Studies. Oxford University Press. 225-245.
Ritchie, G. 2007. Some empirical criteria for attributing creativity to a computer program. Minds and Machines 17:67-99.
Reichert, D. P., Seriès, P., & Storkey, A. J. 2013. Charles bonnet syndrome: evidence for a generative model in the cortex? PLoS Computational Biology 9(7):e1003134.
Riesenhuber, M., & Poggio, T. 1999. Hierarchical models of object recognition in cortex. Nature Neuroscience 2(11):1019–1025.
Rigau, J., Feixas, M., & Sbert, M. 2008. Informational aesthetics measures. IEEE Computer Graphics and Applications 28(2):24–34.
Salakhutdinov, R., & Hinton, G. E. 2009. Deep Boltzmann machines. In Proc. of the International Conference on Artificial Intelligence and Statistics, Vol. 5, 448–455.
Simonyan, K., & Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.
Stewart, T. C., Choo, F.-X., & Eliasmith, C. 2012. Spaun: A perception-cognition-action model using spiking neurons. In Proc. Conf. of the Cognitive Science Society, 1018-1023.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., … Rabinovich, A. 2015. Going deeper with convolutions. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1–9.
Takala, T. 2015. Preconceptual Creativity. In Proc. of the Sixth Int. Conf. on Computational Creativity, 252-259.
Thagard, P., & Stewart, T. C. 2011. The AHA! experience: Creativity through emergent binding in neural networks. Cognitive Science 35(1):1-33.
Xiao, P., & Linkola, S. 2015. Vismantic: Meaning-making with Images. In Proceedings of the Sixth International Conference on Computational Creativity, 158-165.
Zeki, S. 2001. Essays on science and society: Artistic creativity and the brain. Science 293(5527):51–52.
Zorzi, M., Testolin, A., & Stoianov, I. P. 2013. Modeling language and cognition with deep unsupervised learning: a tutorial overview. Frontiers in Psychology 4.
