Using an Ensemble of Incrementally Fine-Tuned CNNs for Cross-Domain Object Category Recognition
文献类型：期刊论文
| 作者 | Yan F(闫飞)3; Zhang, Xuesong3,4; Zhuang Y( 庄严)3; Hu, Huosheng2; Bu CG(卜春光)1
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| 刊名 | IEEE ACCESS
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| 出版日期 | 2019 |
| 卷号 | 7页码:33822-33833 |
| 关键词 | Convolutional neural network object category recognition ensemble learning transfer learning |
| ISSN号 | 2169-3536 |
| 产权排序 | 4 |
| 英文摘要 | When the training data is inadequate, it is difficult to train a deep Convolutional Neural Network (CNN) from scratch with randomized initial weights. Instead, it is common to train a source CNN model on a very large data set beforehand, and then use the learned source CNN model as an initialization to train a target CNN model. In deep learning realm, this procedure is called fine-tuning a CNN. This paper presents an experimental study on how to combine a collection of incrementally fine-tuned CNN models for cross-domain and multi-class object category recognition tasks. A group of fine-tuned CNN models is trained on the target data set by incrementally transferring parameters from a source CNN model trained on a large data set initially. The last two fully-connected (FC) layers of the source CNN model are eliminated, and two New FC layers are added to make the learned new CNN model suitable for the target task. Based on Caltech-101 and Office data sets, the experimental results demonstrate the effectiveness and good performance of the proposed methods. The proposed method is more suitable for the object recognition task when there is inadequate target training data. |
| 语种 | 英语 |
| WOS记录号 | WOS:000463478700001 |
| 资助机构 | National Natural Science Foundation of China [61503056, U1508208] ; Fundamental Scienti~c Research Project of Liaoning Provincial Department of Education [JDL2017017] ; National Science Foundation of Liaoning Province of China [20180551020] ; State Key Laboratory of Robotics [2017-O15] |
| 源URL | [http://ir.sia.cn/handle/173321/24588]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Yan F(闫飞) |
| 作者单位 | 1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2.School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, U.K. 3.School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China 4.Software Technology Institute, Dalian Jiaotong University, Dalian 116028, China |
| 推荐引用方式 GB/T 7714 | Yan F,Zhang, Xuesong,Zhuang Y,et al. Using an Ensemble of Incrementally Fine-Tuned CNNs for Cross-Domain Object Category Recognition[J]. IEEE ACCESS,2019,7:33822-33833. |
| APA | Yan F,Zhang, Xuesong,Zhuang Y,Hu, Huosheng,&Bu CG.(2019).Using an Ensemble of Incrementally Fine-Tuned CNNs for Cross-Domain Object Category Recognition.IEEE ACCESS,7,33822-33833. |
| MLA | Yan F,et al."Using an Ensemble of Incrementally Fine-Tuned CNNs for Cross-Domain Object Category Recognition".IEEE ACCESS 7(2019):33822-33833. |
入库方式： OAI收割
来源：沈阳自动化研究所
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