  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.
