论文标题
用振荡激活函数评估CNN
Evaluating CNN with Oscillatory Activation Function
论文作者
论文摘要
从图像中学习高维复合特征的CNN能力背后的原因是激活函数引入的非线性。已经发现了几种高级激活功能以改善神经网络的训练过程，因为选择激活功能是建模的关键步骤。最近的研究提出了使用振荡激活功能来解决受人脑皮层启发的分类问题。本文使用振荡激活函数（GCU）探讨了CNN体系结构之一Alexnet在MNIST和CIFAR10数据集上的性能以及其他一些常用的激活功能，例如Relu，Prelu和Mish。
The reason behind CNNs capability to learn high-dimensional complex features from the images is the non-linearity introduced by the activation function. Several advanced activation functions have been discovered to improve the training process of neural networks, as choosing an activation function is a crucial step in the modeling. Recent research has proposed using an oscillating activation function to solve classification problems inspired by the human brain cortex. This paper explores the performance of one of the CNN architecture ALexNet on MNIST and CIFAR10 datasets using oscillatory activation function (GCU) and some other commonly used activation functions like ReLu, PReLu, and Mish.
