visualizing and understanding convolutional networks
时间: 2023-04-29 21:01:22 浏览: 81
卷积神经网络(Convolutional Neural Network, CNN)是一种用于图像识别和处理的深度学习模型。它通过不断地进行卷积操作和池化操作来提取图像的特征。可视化CNN是理解它的工作原理和调整超参数的有效方法之一。常见的可视化方法有:网络结构可视化、卷积核可视化、特征图可视化、可解释性可视化等。
相关问题
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。
图像识别有关的文献推荐
我建议你可以参考以下文献:
1. "Deep Learning for Visual Recognition" by Andrew Ng and Pieter Abbeel
2. "Convolutional Neural Networks for Visual Recognition" by Fei-Fei Li, Justin Johnson, and Serena Yeung
3. "ImageNet Classification with Deep Convolutional Neural Networks" by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton
4. "Visualizing and Understanding Convolutional Networks" by Matthew Zeiler and Rob Fergus
5. "Generative Adversarial Networks" by Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio.