very deep convolutional networks for large-scale image recognition
时间: 2023-05-01 12:01:45 浏览: 81
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。
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VGG16网络英文全程
VGG16 is a convolutional neural network architecture that was proposed by the Visual Geometry Group (VGG) at the University of Oxford. It is named after the group and the number of layers it has, which is 16. VGG16 is widely used for image classification tasks.
The full name of VGG16 is "Very Deep Convolutional Networks for Large-Scale Image Recognition". It was introduced in the paper titled "Very Deep Convolutional Networks for Large-Scale Image Recognition" by Karen Simonyan and Andrew Zisserman in 2014.
VGG16 consists of 16 layers, including 13 convolutional layers and 3 fully connected layers. The convolutional layers are responsible for extracting features from the input image, while the fully connected layers are used for classification. VGG16 has a fixed input size of 224x224 pixels.
The architecture of VGG16 is characterized by its simplicity and uniformity. It uses small 3x3 convolutional filters throughout the network, which helps in capturing local patterns effectively. The network also uses max poo
卷积神经网络反向传播有没有推荐的论文
以下是卷积神经网络反向传播相关的经典论文:
1. Backpropagation Through Time: What It Does and How to Do It Efficiently (1990) by Paul Werbos
2. Learning Representations by Back-Propagating Errors (1986) by David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams
3. Convolutional Neural Networks for Image Classification (2012) by Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton
4. Visualizing and Understanding Convolutional Networks (2014) by Matthew D. Zeiler and Rob Fergus
5. Deep Residual Learning for Image Recognition (2016) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (2014) by Karen Simonyan and Andrew Zisserman
7. Going Deeper with Convolutions (2015) by Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich
以上论文都是关于卷积神经网络反向传播的经典之作,对于深入了解卷积神经网络反向传播非常有帮助。