Most modern convolutional neural networks (CNNs) used for object recognition
are built using the same principles: Alternating convolution and max-pooling
layers followed by a small number of fully connected layers. We re-evaluate the
state of the art for object recognition from small images with convolutional
networks, questioning the necessity of different components in the pipeline. We
find that max-pooling can simply be replaced by a convolutional layer with
increased stride without loss in accuracy on several image recognition
benchmarks. Following this finding -- and building on other recent work for
finding simple network structures -- we propose a new architecture that
consists solely of convolutional layers and yields competitive or state of the
art performance on several object recognition datasets (CIFAR-10, CIFAR-100,
ImageNet). To analyze the network we introduce a new variant of the
"deconvolution approach" for visualizing features learned by CNNs, which can be
applied to a broader range of network structures than existing approaches.

通过重新评估小图像物体识别的状态并提出新的 CNN 网络结构，本文发现在保持精度的情况下，最大池化层可以简单地用增加步长的卷积层取代，并引入了一种新型的变体来可视化 CNN 学习到的特征。