Is Each Layer Non-trivial in CNN?

Is Each Layer Non-trivial in CNN?

Convolutional neural network (CNN) models have achieved great success in many fields. With the advent of ResNet, networks used in practice are getting deeper and wider.However, is each layer non-trivial in networks? To answer this question, we trained a network on the training set, then we replace the network convolution kernels with zeros and test the result models on the test set. We compared experimental results with baseline and showed that we can reach similar or even the same performances. Although convolution kernels are the cores of networks, we demonstrate that some of them are trivial and regular in ResNet.

CNN中的每一层都很重要吗？

卷积神经网络（CNN）模型在许多领域都取得了巨大的成功。随着ResNet的出现，在实践中使用的网络越来越深入。.. 但是，网络中的每一层都是不平凡的吗？为了回答这个问题，我们在训练集上训练了一个网络，然后将网络卷积核替换为零，并在测试集上测试了结果模型。我们将实验结果与基线进行了比较，结果表明我们可以达到相似甚至相同的性能。尽管卷积内核是网络的核心，但我们证明了其中的一些在ResNet中是微不足道的。 （阅读更多）