Informing artificial intelligence generative techniques using cognitive theories of human creativity

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Abstract

The common view that our creativity is what makes us uniquely human suggests that incorporating research on human creativity into generative deep learning techniques might be a fruitful avenue for making their outputs more compelling and human-like. Using an original synthesis of DeepDream-based convolutional neural networks and cognitive based computational art rendering systems, we show how honing theory, intrinsic motivation, and the notion of a "seed incident" can be implemented computationally, and demonstrate their impact on the resulting generative art. Conversely, we discuss how explorations in deep learning convolutional neural net generative systems can inform our understanding of human creativity. We conclude with ideas for further cross-fertilization between AI based computational creativity and psychology of creativity.

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Dipaola, S., Gabora, L., & McCaig, G. (2018). Informing artificial intelligence generative techniques using cognitive theories of human creativity. In Procedia Computer Science (Vol. 145, pp. 158–168). Elsevier B.V. https://doi.org/10.1016/j.procs.2018.11.024

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