Deep Convolutional Neural Networks are able to identify complex patterns and perform tasks with super-human capabilities. However, besides the exceptional results, they are not completely understood and it is still impractical to hand-engineer similar solutions. In this work, an image classification Convolutional Neural Network and its building blocks are described from a frequency domain perspective. Some network layers have established counterparts in the frequency domain like the convolutional and pooling layers. We propose the 2SReLU layer, a novel non-linear activation function that preserves high frequency components in deep networks. A convolution-free network is presented, and it is demonstrated that in the frequency domain it is possible to achieve competitive results without using the computationally costly convolution operation. A source code implementation in PyTorch is provided at: https://gitlab.com/thomio/2srelu.

深度卷积神经网络能够识别复杂的模式并以超人的能力执行任务。然而，除了特殊的结果之外，它们还没有被完全理解，手工设计类似的解决方案仍然不切实际。在这项工作中，从频域的角度描述了图像分类卷积神经网络及其构建块。一些网络层在频域中建立了对应物，如卷积层和池化层。我们提出了 2SReLU 层，这是一种新颖的非线性激活函数，可以保留深层网络中的高频分量。提出了一个无卷积网络，并证明在频域中可以在不使用计算成本高的卷积操作的情况下获得有竞争力的结果。