Creativity, a process that generates novel and meaningful ideas, involves increased association between task-positive (control) and task-negative (default) networks in the human brain. Inspired by this seminal finding, in this study we propose a creative decoder within a deep generative framework, which involves direct modulation of the neuronal activation pattern after sampling from the learned latent space. The proposed approach is fully unsupervised and can be used off-the-shelf. Several novelty metrics and human evaluation were used to evaluate the creative capacity of the deep decoder. Our experiments on different image datasets (MNIST, FMNIST, MNIST+FMNIST, WikiArt and CelebA) reveal that atypical co-activation of highly activated and weakly activated neurons in a deep decoder promotes generation of novel and meaningful artifacts.
CITATION STYLE
Das, P., Quanz, B., Chen, P. Y., Ahn, J. W., & Shah, D. (2020). Toward a neuro-inspired creative decoder. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2021-January, pp. 2746–2753). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2020/381
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