CASIA OpenIR  > 学术期刊  > International Journal of Automation and Computing
Why and When Can Deep-but Not Shallow-networks Avoid the Curse of Dimensionality: A Review
Tomaso Poggio1; Hrushikesh Mhaskar2,3; Lorenzo Rosasco1; Brando Miranda1; Qianli Liao1
Source PublicationInternational Journal of Automation and Computing
ISSN1476-8186
2017
Volume14Issue:5Pages:503-519
SubtypeIJAC-HIC-2016-11-271.pdf
AbstractThe deep learning technology has shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation. In particular, recent advances of deep learning techniques bring encouraging performance to ¯ne-grained image classi¯cation which aims to distinguish subordinate-level categories, such as bird species or dog breeds. This task is extremely challenging due to high intra-class and low inter-class variance. In this paper, we review four types of deep learning based fine-grained image classification approaches, including the general convolutional neural networks (CNNs), part detection based, ensemble of networks based and visual attention based ¯ne-grained image classi¯cation approaches. Besides, the deep learning based semantic segmentation approaches are also covered in this paper. The region proposal based and fully convolutional networks based approaches for semantic segmentation are introduced respectively.
KeywordDeep learning fine-grained image classification semantic segmentation convolutional neural network (CNN) recurrent neural network (RNN).
DOI10.1007/s11633-017-1054-2
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Cited Times:91[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/42475
Collection学术期刊_International Journal of Automation and Computing
Affiliation1.Center for Brains, Minds, and Machines, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
2.Department of Mathematics, California Institute of Technology, Pasadena, CA 91125, USA
3.Institute of Mathematical Sciences, Claremont Graduate University, Claremont, CA 91711, USA
Recommended Citation
GB/T 7714
Tomaso Poggio,Hrushikesh Mhaskar,Lorenzo Rosasco,et al. Why and When Can Deep-but Not Shallow-networks Avoid the Curse of Dimensionality: A Review[J]. International Journal of Automation and Computing,2017,14(5):503-519.
APA Tomaso Poggio,Hrushikesh Mhaskar,Lorenzo Rosasco,Brando Miranda,&Qianli Liao.(2017).Why and When Can Deep-but Not Shallow-networks Avoid the Curse of Dimensionality: A Review.International Journal of Automation and Computing,14(5),503-519.
MLA Tomaso Poggio,et al."Why and When Can Deep-but Not Shallow-networks Avoid the Curse of Dimensionality: A Review".International Journal of Automation and Computing 14.5(2017):503-519.
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