Toward A Neuro-inspired Creative Decoder. (arXiv:1902.02399v1 [cs.AI])

Creativity, a process that generates novel and valuable ideas, involves
increased association between task-positive (control) and task-negative
(default) networks in brain. Inspired by this seminal finding, in this study we
propose a creative decoder that directly modulates the neuronal activation
pattern, while sampling from the learned latent space. The proposed approach is
fully unsupervised and can be used as off-the-shelf. Our experiments on three
different image datasets (MNIST, FMNIST, CELEBA) reveal that the co-activation
between task-positive and task-negative neurons during decoding in a deep
neural net enables generation of novel artifacts. We further identify
sufficient conditions on several novelty metrics towards measuring the
creativity of generated samples.

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