Open Category Classification by Adversarial Sample Generation
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In real-world classification tasks, it is difficult to collect training samples from all possible categories of the environment. Therefore, when an instance of an unseen class appears in the prediction stage, a robust classifier should be able to tell that it is from an unseen class, instead of classifying it to be any known category. In this paper, adopting the idea of adversarial learning, we propose the ASG framework for open-category classification. ASG generates positive and negative samples of seen categories in the unsupervised manner via an adversarial learning strategy. With the generated samples, ASG then learns to tell seen from unseen in the supervised manner. Experiments performed on several datasets show the effectiveness of ASG.
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Use of Generative Adversarial Networks is a particularly fruitful approach for detecting out-of-sample cases.
“A particularly fruitful approach seems to be the use of Generative Adversarial Networks (GANs) for detecting out-of-sample cases.[24][25][26]”
From The need for uncertainty quantification in machine-assisted medical decision making (2019) · cites this paper as [25]
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Dai, R. (2022) · theses.fr (ABES) · doi:10.70675/85961b38z6178z43bfz989fz30047aa23c9c
Minimal discriminative learning for open set recognition
Meng, Y., Ren, H., Liu, L., Wu, M., & Qian, S. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02323-9
- Authors
- Yu, Y., Qu, W.-Y., Li, N., & Guo, Z.
- Affiliations
- National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
- Nanjing University (CN)
- Alibaba Group (China) (CN)
- University of California, Berkeley (US)
- ORCID
- Li, Nan
- Venue
- Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
- Published
- 2017-08
- Pages
- 3357-3363
- OpenAlex
- W2962914604
- Cited by
- 84
- References
- 23
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 16 · 2023: 18 · 2022: 7 · 2021: 6 · 2020: 7 · 2019: 8 · 2018: 3
- FWCI
- 2.25
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61333014, 61375061)
- Nanjing University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- open-set recognition, adversarial sample generation, adversarial learning, unseen class detection, robust classification
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