论文标题
将图像梯度合并为与输入图像相关的辅助输入，以提高CNN模型的性能
Incorporating Image Gradients as Secondary Input Associated with Input Image to Improve the Performance of the CNN Model
论文作者
论文摘要
CNN在现代是非常流行的神经网络架构。它主要是与视觉相关任务的最常用工具，可以从给定图像中提取重要功能。此外，CNN用作过滤器，可以使用不同层的卷积操作提取重要特征。在现有的CNN体系结构中，要通过给定输入训练网络，只有单一形式的给定输入形式被馈送到网络中。在本文中，已经提出了新的体系结构，即通过与两种形式的输入共享层，以多种形式通过多种形式传递给定输入。我们将图像梯度合并为与原始输入图像关联的输入的第二形式，并允许使用相同数量的参数在网络中流动两个输入，以改善模型的性能以更好地泛化。提出的CNN体系结构的结果适用于MNIST，CIFAR10和CIFAR100等各种数据集的结果，与基准CNN体系结构相比，考虑到单一形式的输入相比，结果优越。
CNN is very popular neural network architecture in modern days. It is primarily most used tool for vision related task to extract the important features from the given image. Moreover, CNN works as a filter to extract the important features using convolutional operation in distinct layers. In existing CNN architectures, to train the network on given input, only single form of given input is fed to the network. In this paper, new architecture has been proposed where given input is passed in more than one form to the network simultaneously by sharing the layers with both forms of input. We incorporate image gradient as second form of the input associated with the original input image and allowing both inputs to flow in the network using same number of parameters to improve the performance of the model for better generalization. The results of the proposed CNN architecture, applying on diverse set of datasets such as MNIST, CIFAR10 and CIFAR100 show superior result compared to the benchmark CNN architecture considering inputs in single form.
