  In this study, we attempt to model intuition and incorporate this formalism
to improve the performance of the Convolutional Neural Networks. Despite
decades of research, ambiguities persist on principles of intuition.
Experimental psychology reveals many types of intuition, which depend on state
of the human mind. We focus on visual intuition, useful for completing missing
information during visual cognitive tasks. First, we set up a scenario to
gradually decrease the amount of visual information in the images of a dataset
to examine its impact on CNN accuracy. Then, we represent a model for visual
intuition using Gestalt theory. The theory claims that humans derive a set of
templates according to their subconscious experiences. When the brain decides
that there is missing information in a scene, such as occlusion, it
instantaneously completes the information by replacing the missing parts with
the most similar ones. Based upon Gestalt theory, we model the visual
intuition, in two layers. Details of these layers are provided throughout the
paper. We use the MNIST data set to test the suggested intuition model for
completing the missing information. Experiments show that the augmented CNN
architecture provides higher performances compared to the classic models when
using incomplete images.
