  In this paper, we approach the problem of detecting trypophobia triggers
using Convolutional neural networks. We show that standard architectures such
as VGG or ResNet are capable of recognizing trypophobia patterns. We also
conduct experiments to analyze the nature of this phenomenon. To do that, we
dissect the network decreasing the number of its layers and parameters. We
prove, that even significantly reduced networks have accuracy above 91% and
focus their attention on the trypophobia patterns as presented on the visual
explanations.
