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.
翻译:创造性是一个产生新颖和宝贵想法的过程,它涉及加强大脑中任务正(控制)和任务负(默认)网络之间的联系。受这一开创性发现的影响,我们在本研究中提议了一种创造性解码器,直接调节神经激活模式,同时从已学的潜在空间取样。拟议方法完全不受监督,可以作为现成使用。我们在三个不同的图像数据集(MNIST、FMNIST、CELBA)上的实验显示,任务正(控制)和任务负(默认)神经元在深层神经网解码期间共同活动,使得新工艺品的生成得以进行。我们进一步确定若干新颖的计量标准是否足以衡量所产生样品的创造力。