very deep convolutional networks for large-scale image recognition

深度卷积网络是用于大规模图像识别的常用技术。它们通过不断地堆叠卷积层和池化层来增加网络的深度，从而能够更好地提取图像中的特征。在训练过程中，网络会自动学习到图像中的高级特征，如边缘、纹理、形状等，从而提高识别精度。

相关问题

Very Deep Convolutional Networks for Large-Scale Image Recognition" by Karen Simonyan and Andrew Zisserman (2014)

"Very Deep Convolutional Networks for Large-Scale Image Recognition" is a research paper by Karen Simonyan and Andrew Zisserman, which was published in 2014. The paper introduces the architecture of the Convolutional Neural Network (CNN) called VGGNet, which achieved state-of-the-art results in image classification tasks during the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014.

VGGNet is a deep CNN architecture that consists of 16-19 layers, with a simple and uniform structure. The network has small convolution filters of size 3x3, which are stacked on top of each other to form deeper representations. The authors showed that increasing the depth of the network leads to better performance, but at a cost of increased computational complexity.

The paper also introduced a method for visualizing the learned features of the network, which helped to understand how the network was able to achieve high performance on image classification tasks. The authors used the learned features of the network to generate images that maximally activate certain neurons in the network, which provided insights into the types of patterns that the network was able to recognize.

Overall, the VGGNet architecture has become a popular benchmark in the field of deep learning, and has been used as a starting point for many subsequent CNN architectures.