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The common view that our creativity is what makes us uniquely human suggeststhat incorporating research on human creativity into generative deep learningtechniques might be a fruitful avenue for making their outputs more compellingand human-like. Using an original synthesis of Deep Dream-based convolutionalneural networks and cognitive based computational art rendering systems, weshow how honing theory, intrinsic motivation, and the notion of a 'seedincident' can be implemented computationally, and demonstrate their impact onthe resulting generative art. Conversely, we discuss how explorations in deeplearn-ing convolutional neural net generative systems can inform ourunderstanding of human creativity. We conclude with ideas for furthercross-fertilization between AI based computational creativity and psychology ofcreativity.

Informing Artificial Intelligence Generative Techniques using Cognitive Theories of Human Creativity