Unified Classification and Rejection:A One-versus-all Framework

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外文摘要：Classifying patterns of known classes and rejecting ambiguous and novel(also called as out-of-distribution(OOD))inputs are involved in open world pattern recognition.Deep neural network models usually excel in closed-set classification while perform poorly in rejecting OOD inputs.To tackle this problem,numerous methods have been designed to perform open set recognition(OSR)or OOD rejection/detection tasks.Previous methods mostly take post-training score transformation or hybrid models to ensure low scores on OOD inputs while separating known classes.In this paper,we attempt to build a unified framework for building open set classi-fiers for both classification and OOD rejection.We formulate the open set recognition of K-known-class as a(K+1)-class classification problem with model trained on known-class samples only.By decomposing the K-class problem into K one-versus-all(OVA)binary clas-sification tasks and binding some parameters,we show that combining the scores of OVA classifiers can give(K+1)-class posterior prob-abilities,which enables classification and OOD rejection in a unified framework.To maintain the closed-set classification accuracy of the OVA trained classifier,we propose a hybrid training strategy combining OVA loss and multi-class cross-entropy loss.We implement the OVA framework and hybrid training strategy on the recently proposed convolutional prototype network and prototype classifier on vis-ion transformer(ViT)backbone.Experiments on popular OSR and OOD detection datasets demonstrate that the proposed framework,using a single multi-class classifier,yields competitive performance in closed-set classification,OOD detection,and misclassification de-tection.The code is available at https://github.com/zhen-cheng121/CPN_OVA_unified.

外文关键词：

Open set recognitionout-of-distribution detectionmisclassification detectionconvolutional prototype networkone-versus-allDempster-Shafer theory of evidence

作者：

Zhen Cheng、Xu-Yao Zhang、Cheng-Lin Liu

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作者单位：

State Key Laboratory of Multimodal Artificial Intelligence Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China

School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China

基金：

National Key Research and Development Program,ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

项目编号：

2018 AAA0100400U20A202236222260962076236

出版年：

2024

DOI：

10.1007/s11633-024-1514-4

机器智能研究(英文)

中国科学院自动化所

机器智能研究(英文)

CSTPCDEI

影响因子：0.49

ISSN：2731-538X

年,卷(期)：2024.21(5)