论文标题
图像分类中的可训练激活功能
Trainable Activation Function in Image Classification
论文作者
论文摘要
在当前的神经网络研究中，激活函数是由人手动指定的，在训练过程中无法改变自己。本文着重于如何使激活功能可训练深度神经网络。我们使用不同激活功能的系列和线性组合使激活函数连续变量。此外，我们在CIFAR-10数据集上使用傅立叶系列模拟激活（Fourier-CNN）和具有线性组合激活函数（LC-CNN）的CNN测试CNN的性能。结果表明，我们的可训练激活功能比最常用的Relu激活函数揭示了更好的性能。最后，我们通过自动编码器提高了傅立叶-CNN的性能，并测试PSO算法在优化网络参数时的性能
In the current research of neural networks, the activation function is manually specified by human and not able to change themselves during training. This paper focus on how to make the activation function trainable for deep neural networks. We use series and linear combination of different activation functions make activation functions continuously variable. Also, we test the performance of CNNs with Fourier series simulated activation(Fourier-CNN) and CNNs with linear combined activation function (LC-CNN) on Cifar-10 dataset. The result shows our trainable activation function reveals better performance than the most used ReLU activation function. Finally, we improves the performance of Fourier-CNN with Autoencoder, and test the performance of PSO algorithm in optimizing the parameters of networks
