论文标题
使用Imagenet网络中间层对恐怖恐惧症触发的响应进行建模
Modelling response to trypophobia trigger using intermediate layers of ImageNet networks
论文作者
论文摘要
在本文中，我们解决了使用卷积神经网络检测恐怖恐惧症触发因素的问题。我们表明，诸如VGG或Resnet之类的标准体系结构能够识别恐怖恐惧症模式。我们还进行实验以分析这种现象的性质。为此，我们剖析网络减少其层和参数的数量。我们证明，即使是显着降低的网络的精度超过91％，并将注意力集中在视觉解释中所列出的恐怖恐惧症模式上。
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.
