BundleNet Learning with Noisy Label via Sample Correlations
Li, Chenghua1,4; Zhang, Chunjie2,5; Ding, Kun2,4; Li, Gang1,4; Cheng, Jian1,4,6; Lu, Hanqing3,4; Jian Cheng
2018
发表期刊IEEE ACCESS
卷号6期号:1页码:2367-2377
文章类型Article
摘要Sequential patterns are important, because they can be exploited to improve the prediction accuracy of our classifiers. Sequential data, such as time series/video frames, and event data are becoming more and more ubiquitous in a wide spectrum of application scenarios especially in the background of large data and deep learning. However, large data sets used in training modern machine-learning models, such as deep neural networks, are often affected by label noise. Existing noisy learning approaches mainly focus on building an additional network to clean the noise or find a robust loss function. Few works tackle this problem by exploiting sample correlations. In this paper, we propose BundleNet, a framework of sequential structure (named bundle-module, see Fig. 1) for deep neural networks to handle the label noise. The bundle module naturally takes into account sample correlations by constructing bundles of samples class-by-class, and treats them as independent inputs. Moreover, we prove that the bundle-module performs a form of regularization, which is similar to dropout as regularization during training. The regularization effect endows the BundleNet with strong robustness to the label noise. Extensive experiments on public data sets prove that the proposed approach is effective and promising.
关键词Bundlenet Sequential Data Classification Noisy Label Regularization
WOS标题词Science & Technology ; Technology
DOI10.1109/ACCESS.2017.2782844
关键词[WOS]IMAGE CLASSIFICATION ; REPRESENTATION
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61332016) ; Jiangsu Key Laboratory of Big Data Analysis Technology ; 863 Program(2014AA015105)
WOS研究方向Computer Science ; Engineering ; Telecommunications
WOS类目Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号WOS:000426275700001
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20899
专题模式识别国家重点实验室_图像与视频分析
通讯作者Jian Cheng
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Pattern Recognit & Intelligent Syst, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
6.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China
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Li, Chenghua,Zhang, Chunjie,Ding, Kun,et al. BundleNet Learning with Noisy Label via Sample Correlations[J]. IEEE ACCESS,2018,6(1):2367-2377.
APA Li, Chenghua.,Zhang, Chunjie.,Ding, Kun.,Li, Gang.,Cheng, Jian.,...&Jian Cheng.(2018).BundleNet Learning with Noisy Label via Sample Correlations.IEEE ACCESS,6(1),2367-2377.
MLA Li, Chenghua,et al."BundleNet Learning with Noisy Label via Sample Correlations".IEEE ACCESS 6.1(2018):2367-2377.
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