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In this paper, we approach the problem of detecting trypophobia triggersusing Convolutional neural networks. We show that standard architectures suchas VGG or ResNet are capable of recognizing trypophobia patterns. We alsoconduct experiments to analyze the nature of this phenomenon. To do that, wedissect the network decreasing the number of its layers and parameters. Weprove, that even significantly reduced networks have accuracy above 91% andfocus their attention on the trypophobia patterns as presented on the visualexplanations.

Modelling response to trypophobia trigger using intermediate layers of ImageNet networks