Convolutional Neural Networks (CNNs) have been proven to be extremely successful at solving computer vision tasks. State-of-the-art methods favor such deep network architectures for its accuracy performance, with the cost of having massive number of parameters and high weights redundancy. Previous works have studied how to prune such CNNs weights. In this paper, we go to another extreme and analyze the performance of a network stacked with a single convolution kernel across layers, as well as other weights sharing techniques. We name it Deep Anchored Convolutional Neural Network (DACNN). Sharing the same kernel weights across layers allows to reduce the model size tremendously, more precisely, the network is compressed in memory by a factor of L, where L is the desired depth of the network, disregarding the fully connected layer for prediction. The number of parameters in DACNN barely increases as the network grows deeper, which allows us to build deep DACNNs without any concern about memory costs. We also introduce a partial shared weights network (DACNN-mix) as well as an easy-plug-in module, coined regulators, to boost the performance of our architecture. We validated our idea on 3 datasets: CIFAR-10, CIFAR-100 and SVHN. Our results show that we can save massive amounts of memory with our model, while maintaining a high accuracy performance.

本文介绍了一种名为Deep Anchored Convolutional Neural Network (DACNN)的卷积神经网络架构，它使用单个卷积核并采用权重共享技术进行网络压缩，同时增加了一个名为regulators的易于插入的模块来提高性能，在CIFAR-10、CIFAR-100和SVHN数据集上验证了其准确性与显著的内存节省。

深度锚定卷积神经网络