论文标题
Goldilocks神经网络
Goldilocks Neural Networks
论文作者
论文摘要
我们引入了新的“ Goldilocks”类激活函数类别，仅当输入信号在适当的范围内时，该功能仅在本地内局部变形。信号的局部变形较小，可以更好地理解信号如何通过层转换。 CIFAR-10和CIFAR-100数据集的数值结果表明，Goldilocks网络的性能要比SELU和RELU的表现更好，同时通过层引入数据变形的障碍。
We introduce the new "Goldilocks" class of activation functions, which non-linearly deform the input signal only locally when the input signal is in the appropriate range. The small local deformation of the signal enables better understanding of how and why the signal is transformed through the layers. Numerical results on CIFAR-10 and CIFAR-100 data sets show that Goldilocks networks perform better than, or comparably to SELU and RELU, while introducing tractability of data deformation through the layers.
