论文标题
关于高频和低频信息下卷积神经网络的性能
On the Performance of Convolutional Neural Networks under High and Low Frequency Information
论文作者
论文摘要
近年来，卷积神经网络（CNN）表现出非常有希望的表现，包括对象识别，面部识别，医学图像分析等。但是，通常对经过训练的CNN模型进行了测试，这与训练有素的集合非常相似。 CNN模型的普遍性和鲁棒性是使其适用于看不见数据的非常重要的方面。在这封信中，我们研究了CNN模型在图像的高频信息上的性能。我们观察到训练有素的CNN无法概括高频和低频图像。为了使CNN与高频图像和低频图像具有鲁棒性，我们提出了基于随机滤波的数据增强训练期间。通过提出的基于随机滤波的数据增强方法，根据高频和低频的概括和鲁棒性观察到了令人满意的性能改善。实验是使用CIFAR-10数据集上的RESNET50模型和小型ImageNet数据集上的RESNET101模型进行的。
Convolutional neural networks (CNNs) have shown very promising performance in recent years for different problems, including object recognition, face recognition, medical image analysis, etc. However, generally the trained CNN models are tested over the test set which is very similar to the trained set. The generalizability and robustness of the CNN models are very important aspects to make it to work for the unseen data. In this letter, we study the performance of CNN models over the high and low frequency information of the images. We observe that the trained CNN fails to generalize over the high and low frequency images. In order to make the CNN robust against high and low frequency images, we propose the stochastic filtering based data augmentation during training. A satisfactory performance improvement has been observed in terms of the high and low frequency generalization and robustness with the proposed stochastic filtering based data augmentation approach. The experimentations are performed using ResNet50 model over the CIFAR-10 dataset and ResNet101 model over Tiny-ImageNet dataset.
