训练后卷积神经网络的超平面排列存在偏置
Hyperplane Arrangements of Trained ConvNets Are Biased
AI总结：
本文通过研究卷积层超平面排列的统计特性，发现训练后的卷积神经网络显著偏向规则超平面构型，且这种偏置层对CIFAR10、CIFAR100和ImageNet上的验证性能至关重要。
摘要：
我们通过实证研究卷积层所诱导的超平面排列，考察了训练后卷积神经网络在其卷积层预激活空间中学习到的函数的几何性质。我们引入针对已训练网络权重的统计量，以研究局部排列并将其与训练动态相关联。我们观察到，训练后的卷积神经网络在统计上显著偏向于规则的超平面构型。此外，我们发现，对于在CIFAR10、CIFAR100和ImageNet上训练所考虑的架构而言，表现出偏置构型的层对验证性能至关重要。
英文摘要：
We investigate the geometric properties of the functions learned by trained ConvNets in the preactivation space of their convolutional layers, by performing an empirical study of hyperplane arrangements induced by a convolutional layer. We introduce statistics over the weights of a trained network to study local arrangements and relate them to the training dynamics. We observe that trained ConvNets show a significant statistical bias towards regular hyperplane configurations. Furthermore, we find that layers showing biased configurations are critical to validation performance for the architectures considered, trained on CIFAR10, CIFAR100 and ImageNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
