论文标题
通过在接受场中的首选刺激可视化重新NET的解释
Interpretation of ResNet by Visualization of Preferred Stimulus in Receptive Fields
论文作者
论文摘要
图像识别中使用的方法之一是深卷卷神经网络（DCNN）。 DCNN是一种模型，其中通过加深CNN的隐藏层可以极大地提高特征的表达能力。 CNN的结构是基于哺乳动物视觉皮层的模型确定的。有一个称为“残留网络”（RESNET）的模型，该模型具有跳过连接。 Resnet在学习方法方面是一个高级模型，但从生物学角度尚未解释它。在这项研究中，我们研究了ImageNet中分类任务的重新系统的接受场。我们发现Resnet具有定向选择性神经元和双对手颜色神经元。此外，我们建议RESNET第一层中的某些非活性神经元会影响分类任务。
One of the methods used in image recognition is the Deep Convolutional Neural Network (DCNN). DCNN is a model in which the expressive power of features is greatly improved by deepening the hidden layer of CNN. The architecture of CNNs is determined based on a model of the visual cortex of mammals. There is a model called Residual Network (ResNet) that has a skip connection. ResNet is an advanced model in terms of the learning method, but it has not been interpreted from a biological viewpoint. In this research, we investigate the receptive fields of a ResNet on the classification task in ImageNet. We find that ResNet has orientation selective neurons and double opponent color neurons. In addition, we suggest that some inactive neurons in the first layer of ResNet affect the classification task.
