Knowledge Commons of Institute of Automation,CAS
Convolutional Prototype Network for Open Set Recognition | |
Hong-Ming Yang1,2; Xu-Yao Zhang1,2; Fei Yin1,2; Qing Yang1,2; Cheng-Lin Liu1,2,3 | |
发表期刊 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
2022 | |
卷号 | 44期号:5页码:2358-2370 |
摘要 | Despite the success of convolutional neural network (CNN) in conventional closed-set recognition (CSR), it still lacks robustness for dealing with unknowns (those out of known classes) in open environment. To improve the robustness of CNN in open-set recognition (OSR) and meanwhile maintain its high accuracy in CSR, we propose an alternative deep framework called convolutional prototype network (CPN), which keeps CNN for representation learning but replaces the closed-world assumed softmax with an open-world oriented and human-like prototype model. To equip CPN with discriminative ability for classifying known samples, we design several discriminative losses for training. Moreover, to increase the robustness of CPN for unknowns, we interpret CPN from the perspective of generative model and further propose a generative loss, which is essentially maximizing the log-likelihood of known samples and serves as a latent regularization for discriminative learning. The combination of discriminative and generative losses makes CPN a hybrid model with advantages for both CSR and OSR. Under the designed losses, the CPN is trained end-to-end for learning the convolutional network and prototypes jointly. For application of CPN in OSR, we propose two rejection rules for detecting different types of unknowns. Experiments on several datasets demonstrate the efficiency and effectiveness of CPN for both CSR and OSR tasks. |
关键词 | open-set recognition CNN prototype model unknown detection discriminative model generative model |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000792921400012 |
七大方向——子方向分类 | 模式识别基础 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44421 |
专题 | 多模态人工智能系统全国重点实验室_模式分析与学习 |
通讯作者 | Xu-Yao Zhang; Cheng-Lin Liu |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.CAS Center for Excellence of Brain Science and Intelligence Technology |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Hong-Ming Yang,Xu-Yao Zhang,Fei Yin,et al. Convolutional Prototype Network for Open Set Recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2022,44(5):2358-2370. |
APA | Hong-Ming Yang,Xu-Yao Zhang,Fei Yin,Qing Yang,&Cheng-Lin Liu.(2022).Convolutional Prototype Network for Open Set Recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence,44(5),2358-2370. |
MLA | Hong-Ming Yang,et al."Convolutional Prototype Network for Open Set Recognition".IEEE Transactions on Pattern Analysis and Machine Intelligence 44.5(2022):2358-2370. |
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Convolutional Protot(5038KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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