Modelling response to trypophobia trigger using intermediate layers of ImageNet networks

Autor:	Woźnicki, Piotr, Kuźba, Michał, Migdał, Piotr
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: extended abstract submitted to Eastern European Machine Learning 2019, 3 pages, 2 figures, 1 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.08490 Zobrazit plný text záznamu View this record from Arxiv