Explicit Discrimination-driven Automatic Unknown Class Clustering for Open-World Semi-Supervised Learning

SONG Jialun; DU Lan; CHEN Jian

doi:10.11999/JEIT251291

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SONG Jialun, DU Lan, CHEN Jian. Explicit Discrimination-driven Automatic Unknown Class Clustering for Open-World Semi-Supervised Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251291

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SONG Jialun, DU Lan, CHEN Jian. Explicit Discrimination-driven Automatic Unknown Class Clustering for Open-World Semi-Supervised Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251291

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Explicit Discrimination-driven Automatic Unknown Class Clustering for Open-World Semi-Supervised Learning

doi: 10.11999/JEIT251291 cstr: 32379.14.JEIT251291

National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation ofChina (U24B20137, U21B2039, 62201433), Equipment Preresearch Joint Fund of the Ministry of Education (8091B03032401), Fundamental Research Funds for the Central Universities (QTZX23067)

Received Date: 2025-12-08
Accepted Date: 2026-06-24
Rev Recd Date: 2026-06-22

Available Online: 2026-07-04

Abstract

Abstract

Objective Traditional target recognition is generally developed under the closed-set assumption, in which all test classes are assumed to be included in the training set. In real-world scenarios, however, unlabeled unknown classes are commonly encountered. Therefore, recognition systems should simultaneously recognize known classes and discover and cluster unknown classes. To address this challenge, a transductive Open-World Semi-Supervised Learning (OWSSL) method driven by explicit discrimination and automatic unknown-class clustering is proposed. Methods The proposed method is trained using a small set of labeled known-class samples and a large set of unlabeled test samples containing both known and unknown classes. It consists of two complementary modules. The Dynamic Known-Unknown Class Discrimination (DKUCD) module models the known-class boundary distribution using Extreme Value Theory (EVT) and progressively refines the distribution with high-confidence known-class samples during semi-supervised learning to improve known-unknown discrimination. The Neighbor Intersection-Over-Union Cluster Merging (NIOUCM) module automatically clusters high-confidence unknown-class samples by merging neighboring clusters according to their intersection-over-union relationships. The DKUCD and NIOUCM modules are optimized iteratively to improve discrimination and unknown-class clustering jointly. Results and Discussions Experiments conducted on the optical CIFAR-10 dataset and measured radar datasets demonstrate that the proposed method achieves accurate known-class recognition while effectively clustering unknown classes. Conclusions By explicitly discriminating between known and unknown classes and automatically estimating unknown-class clusters, the proposed method improves both known-class recognition and unknown-class clustering under open-world conditions.
- Target recognition,
- Open-world semi-supervised learning,
- Unknown class discrimination,
- Automatic unknown-class clustering,
- Extreme Value Theory

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References(16)

References

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