CASIA OpenIR  > 多模态人工智能系统全国重点实验室  > 机器人视觉
Conditional Feature Generation for Transductive Open-Set Recognition
Jiayin, Sun1,2,3; Qiulei, Dong1,2,3
Source PublicationPattern Recognition
2023-10
Volume146Pages:1-11
Abstract

Open-set recognition (OSR) aims to simultaneously detect unknown-class samples and classify known-class samples. Most of the existing OSR methods are inductive methods, which generally suffer from the domain shift problem that the learned model from the known-class domain might be unsuitable for the unknown-class domain. Addressing this problem, inspired by the success of transductive learning for alleviating the domain shift problem in many other visual tasks, we propose an Iterative Transductive OSR framework, called IT-OSR, which implements three explored modules iteratively, including a reliability sampling module, a feature generation module, and a baseline update module. Specifically at the initialization stage, a baseline method, which could be an arbitrary inductive OSR method, is used for assigning pseudo labels to the test samples. At the iteration stage, based on the consistency of the assigned pseudo labels between the output/logit space and the latent feature space of the baseline method, a dual-space consistent sampling approach is presented in the reliability sampling module for sampling some reliable ones from the test samples. Then in the feature generation module, a conditional dual-adversarial generative network is designed to generate discriminative features of both known and unknown classes. This generative network employs two discriminators for implementing fake/real and known/unknown-class discriminations respectively. And it is trained by jointly utilizing the test samples with their pseudo labels selected in the reliability sampling module and the labeled training samples. Finally in the baseline update module, the above baseline method is updated/re-trained for sample re-prediction by jointly utilizing the generated features, the selected test samples with pseudo labels, and the training samples. Extensive experimental results on both the standard-dataset and the cross-dataset settings demonstrate that the derived transductive methods, by introducing two typical inductive OSR methods into the proposed IT-OSR framework, achieve better performances than 19 state-of-the-art methods in most cases.

Indexed BySCI
Language英语
IS Representative Paper
Sub direction classification目标检测、跟踪与识别
planning direction of the national heavy laboratory人工智能基础前沿理论
Paper associated data
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56589
Collection多模态人工智能系统全国重点实验室_机器人视觉
Corresponding AuthorQiulei, Dong
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Jiayin, Sun,Qiulei, Dong. Conditional Feature Generation for Transductive Open-Set Recognition[J]. Pattern Recognition,2023,146:1-11.
APA Jiayin, Sun,&Qiulei, Dong.(2023).Conditional Feature Generation for Transductive Open-Set Recognition.Pattern Recognition,146,1-11.
MLA Jiayin, Sun,et al."Conditional Feature Generation for Transductive Open-Set Recognition".Pattern Recognition 146(2023):1-11.
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