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What is a Tabby? Interpretable Model Decisions by Learning Attribute-Based Classification Criteria

文献类型：期刊论文

作者Liu, Haomiao1; Wang, Ruiping2; Shan, Shiguang2; Chen, Xilin2
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2021-05-01
卷号43期号:5页码:1791-1807
关键词Cats Prototypes Visualization Task analysis Streaming media Predictive models Scalability Interpretable model visual attributes convolutional neural network classification criteria
ISSN号0162-8828
DOI10.1109/TPAMI.2019.2954501
英文摘要State-of-the-art classification models are usually considered as black boxes since their decision processes are implicit to humans. On the contrary, human experts classify objects according to a set of explicit hierarchical criteria. For example, "tabby is a domestic cat with stripes, dots, or lines", where tabby is defined by combining its superordinate category (domestic cat) and some certain attributes (e.g., has stripes). Inspired by this mechanism, we propose an interpretable Hierarchical Criteria Network (HCN) by additionally learning such criteria. To achieve this goal, images and semantic entities (e.g., taxonomies and attributes) are embedded into a common space, where each category can be represented by the linear combination of its superordinate category and a set of learned discriminative attributes. Specifically, a two-stream convolutional neural network (CNN) is elaborately devised, which embeds images and taxonomies with the two streams respectively. The model is trained by minimizing the prediction error of hierarchy labels on both streams. Extensive experiments on two widely studied datasets (CIFAR-100 and ILSVRC) demonstrate that HCN can learn meaningful attributes as well as reasonable and interpretable classification criteria. Therefore, the proposed method enables further human feedback for model correction as an additional benefit.
资助项目973 Program[2015CB351802] ; Natural Science Foundation of China[61390511] ; Natural Science Foundation of China[61772500] ; Frontier Science Key Research Project CAS[QYZDJ-SSW-JSC009] ; Youth Innovation Promotion Association CAS[2015085]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000637533800022
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/16657]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Chen, Xilin
作者单位1.Huawei EI Innovat Lab, Beijing 100085, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, CAS, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Haomiao,Wang, Ruiping,Shan, Shiguang,et al. What is a Tabby? Interpretable Model Decisions by Learning Attribute-Based Classification Criteria[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2021,43(5):1791-1807.
APA Liu, Haomiao,Wang, Ruiping,Shan, Shiguang,&Chen, Xilin.(2021).What is a Tabby? Interpretable Model Decisions by Learning Attribute-Based Classification Criteria.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,43(5),1791-1807.
MLA Liu, Haomiao,et al."What is a Tabby? Interpretable Model Decisions by Learning Attribute-Based Classification Criteria".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 43.5(2021):1791-1807.

入库方式： OAI收割

来源：计算技术研究所

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