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基于卷积神经网络Caffe框架的图像分类 被引量:2

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摘要 2013年贾扬清博士在Github上发布了一款深度学习框架"Caffe",为众多研究人员和工程师们提供了一套简单易用且性能强大的深度学习开源框架。本文首先对Caffe框架简单介绍,其次阐述深度学习中应用的卷积神经网络原理,最后介绍如何利用Caffe框架进行图像分类。
出处 《电子技术与软件工程》 2017年第24期73-73,共1页 ELECTRONIC TECHNOLOGY & SOFTWARE ENGINEERING
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  • 1Lowe D G. Distinctive image features from scale-invariant keypoints[J].{H}International Journal of Computer Vision,2004,(2):91-110.
  • 2Dalal N,Triggs B. Histograms of oriented gradients for human detection[A].San Diego,CA,USA:IEEE,2005.886-893.
  • 3Ojala T,Pietikainen M,Harwood D. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions[A].Jerusalem,Irsael:IEEE,1994.582-585.
  • 4Matas J,Chum O,Urban M. Robust wide-baseline stereo from maximally stable extremal regions[J].{H}IMAGE AND VISION COMPUTING,2004,(10):761-767.
  • 5Hinton G E,Osindero S,Teh Y W. A fast learning algorithm for deep belief nets[J].{H}Neural Computation,2006,(7):1527-1554.
  • 6Hinton G E. Learning multiple layers of representation[J].{H}Trends in Cognitive Sciences,2007,(10):428-434.
  • 7Hinton G E,Zemel R S. Autoencoders,minimum description length,and Helmholtz free energy[A].Burlington,USA:Morgan Kaufmann,1994.3-10.
  • 8Rumelhart D E,Hinton G E,Williams R J. Learning Representations by Back-Propagating Errors[M].Cogmitive Modeling:MIT Press,2002.213.
  • 9Vincent P,Larochelle H,Bengio Y. Extracting and composing robust features with denoising autoencoders[A].New York,NY,USA:ACM,2008.1096-1103.
  • 10Lee H,Grosse R,Ranganath R. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations[A].New York,NY,USA:ACM,2009.609-616.

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