Knowledge Commons of Institute of Automation,CAS
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 | |
发表期刊 | International Journal of Automation and Computing |
ISSN | 1476-8186 |
2017 | |
卷号 | 14期号:5页码:503-519 |
文章类型 | IJAC-HIC-2016-11-271.pdf |
摘要 | The 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. |
关键词 | Deep learning fine-grained image classification semantic segmentation convolutional neural network (CNN) recurrent neural network (RNN). |
DOI | 10.1007/s11633-017-1054-2 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/42475 |
专题 | 学术期刊_Machine Intelligence Research |
作者单位 | 1.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 |
推荐引用方式 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. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
IJAC-HIC-2016-11-271(1711KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论