论文标题
在深度卷积网络的完全连接层中删除对象选择性单元可改善分类性能
Deleting object selective units in a fully-connected layer of deep convolutional networks improves classification performance
论文作者
论文摘要
灵长类动物视觉皮层中的神经元显示出广泛的刺激选择性。一些神经元仅响应一小部分刺激图像，而另一些神经元以非选择性的方式对许多刺激图像做出反应。目前尚不清楚刺激选择性和非选择性神经元如何有助于视觉对象识别。本文中，我们使用两种类型的深卷积神经网络（DCNN）的完全连接层进行了刺激选择性与单位缺失对任务性能的影响之间的关系。删除刺激选择单位会导致任务执行的略有改进，而删除刺激非选择性单元则导致任务性能显着下降。但是，这些发现并不意味着刺激选择性单元对任务没有用。实际上，当网络由刺激选择性和非选择性单元组成时，获得了更好的性能。
Neurons in the primate visual cortices show a wide range of stimulus selectivity. Some neurons respond to only a small fraction of stimulus images, whereas others respond to many stimulus images in a non-selective manner. It is unclear how stimulus selective and non-selective neurons contribute to visual object recognition. Herein, we examined the relationship between stimulus selectivity and the effect of deletion of units on task performance using fully a connected layer of two types of deep convolutional neural networks (DCNNs). Deleting a stimulus selective unit caused slight improvements of task performance, whereas deleting stimulus non-selective units caused a significant decrease in task performance. However, these findings do not imply that stimulus selective units have no use for the task. Indeed, better performance was obtained when the networks consisted of both stimulus selective and non-selective units.
