Toward a neuro-inspired creative decoder
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 2746-2753. https://doi.org/10.24963/ijcai.2020/381
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.
Machine Learning: Deep Learning
Multidisciplinary Topics and Applications: Art and Music