  The superior performance of deep convolutional networks over high-dimensional
problems have made them very popular for several applications. Despite their
wide adoption, their underlying mechanisms still remain unclear with their
improvement procedures still relying mainly on a trial and error process. We
introduce a novel sensitivity analysis based on the Gestalt theory for giving
insights into the classifier function and intermediate layers. Since Gestalt
psychology stipulates that perception can be a product of complex interactions
among several elements, we perform an ablation study based on this concept to
discover which principles and image context significantly contribute in the
network classification. Our results reveal that ConvNets follow most of the
visual cortical perceptual mechanisms defined by the Gestalt principles at
several levels. The proposed framework stimulates specific feature maps in
classification problems and reveal important network attributes that can
produce more explainable network models.
