Abstract
Few-shot one-class classification (FS-OCC) is an important and challenging problem involving the recognition of a class using a limited number of positive training samples. Data description is essential for solving the FS-OCC problem as it delineates a region that separates positive data from other classes in the feature space. This paper introduces an effective FS-OCC model named Adaptive Hypersphere Data Description (AHDD). AHDD utilizes hypersphere-based data description with a learnable radius to determine the appropriate region for positive samples in the feature space. Both the radius and the feature network are learned concurrently using meta-learning. We propose a loss function for AHDD that enables the mutual adaptation of the radius and feature within a single FS-OCC task. AHDD significantly outperforms other state-of-the-art FS-OCC methods across various benchmarks and demonstrates strong performance on test sets with extreme class imbalance rates. Experimental results indicate that AHDD learns a robust feature representation, and the implementation of an adaptive radius can also improve the existing FS-OCC baselines.
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References
Carey S, Bartlett E (1978) Acquiring a single new word
Long B, Fan J, Huey H, Chai Z, Frank MC (2021) Parallel developmental changes in children’s production and recognition of line drawings of visual concepts
Khan SS, Madden MG (2014) One-class classification: taxonomy of study and review of techniques. Knowl Eng Rev 29(3):345–374
Oza P, Patel VM (2018) One-class convolutional neural network. IEEE Signal Process Lett 26(2):277–281
Wang Y, Yao Q, Kwok JT, Ni LM (2020) Generalizing from a few examples: a survey on few-shot learning. ACM Comput Surv (CSUR) 53(3):1–34
Moya MM, Hush DR (1996) Network constraints and multi-objective optimization for one-class classification. Neural Netw 9(3):463–474
Minter T (1975) Single-class classification. In: LARS Symposia, pp 54
Miller EG, Matsakis NE, Viola PA (2000) Learning from one example through shared densities on transforms. In: Proceedings IEEE conference on computer vision and pattern recognition. CVPR 2000 (Cat. No. PR00662), vol 1, pp 464–471. IEEE
Fei-Fei L, Fergus R, Perona P (2006) One-shot learning of object categories. IEEE Trans Pattern Anal Mach Intell 28(4):594–611
Frikha A, Krompaß D, Köpken H-G, Tresp V (2021) Few-shot one-class classification via meta-learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 7448–7456
Kruspe A (2019) One-way prototypical networks. arXiv:1906.00820
Dahia G, Pamplona Segundo M (2021) Meta learning for few-shot one-class classification. AI 2(2):195–208
Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471
Tax DM, Duin RP (2004) Support vector data description. Mach Learn 54:45–66
Hojjati H, Ho TKK, Armanfard N (2024) Self-supervised anomaly detection in computer vision and beyond: a survey and outlook. Neural Networks, pp 106106
Mohammad S, Arashloo SR (2024) Robust one-class classification using deep kernel spectral regression. Neurocomputing 573:127246
Schlegl T, Seeböck P, Waldstein SM, Schmidt-Erfurth U, Langs G (2017) Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: International conference on information processing in medical imaging, pp 146–157. Springer
Yang X, Li X (2023) Atdad: One-class adversarial learning for tabular data anomaly detection. Comput & Secur 134:103449
Ivanovska M, Štruc V (2024) Y-gan: learning dual data representations for anomaly detection in images. Expert Syst Appl, pp 123410
Arashloo SR, Kittler J (2020) Robust one-class kernel spectral regression. IEEE Trans Neural Netw Learn Syst 32(3):999–1013
Ruff L, Vandermeulen R, Goernitz N, Deecke L, Siddiqui S.A, Binder A, Müller E, Kloft M (2018) Deep one-class classification. In: International conference on machine learning, pp 4393–4402. PMLR
Xing H-J, Zhang P-P (2023) Contrastive deep support vector data description. Pattern Recogn 143:109820
Kim M, Kim J, Yu J, Choi JK (2023) Active anomaly detection based on deep one-class classification. Pattern Recogn Lett 167:18–24
Gharoun H, Momenifar F, Chen F, Gandomi A (2023) Meta-learning approaches for few-shot learning: a survey of recent advances. ACM Computing Surveys
Rao S, Huang J, Tang Z (2024) Rdprotofusion: refined discriminative prototype-based multi-task fusion for cross-domain few-shot learning. Neurocomputing, 128117
Vinyals O, Blundell C, Lillicrap T, Wierstra D,et al (2016) Matching networks for one shot learning. Advances in Neural Information Processing Systems 29
Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Advances in Neural Information Processing Systems 30
Sung F, Yang Y, Zhang L, Xiang T, Torr P.H, Hospedales TM (2018) Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1199–1208
Li X, Li Y, Zheng Y, Zhu R, Ma Z, Xue J-H, Cao J (2023) Renap: relation network with adaptiveprototypical learning for few-shot classification. Neurocomputing 520:356–364
Jia X, Mao Y, Pan Z, Wang Z, Ping P (2024) Few-shot learning based on hierarchical feature fusion via relation networks. Int J Approx Reason 170:109186
Zhou F, Wang P, Zhang L, Wei W, Zhang Y (2023) Revisiting prototypical network for cross domain few-shot learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 20061–20070
Zhao P, Wang L, Zhao X, Liu H, Ji X (2024) Few-shot learning based on prototype rectification with a self-attention mechanism. Expert Syst Appl 249:123586
Lake B, Salakhutdinov R, Gross J, Tenenbaum J (2011) One shot learning of simple visual concepts. In: Proceedings of the annual meeting of the cognitive science society, vol 33
Krizhevsky A, Hinton G et al (2009) Learning multiple layers of features from tiny images
Maaten L, Hinton G (2008) Visualizing data using t-sne. Journal of Machine Learning Research 9(11)
Funding
This work was supported in part by National Natural Science Foundation of China (Grant No.82171965); Clinical and Translational Medical Research Fund of the Chinese Academy of Medical Sciences (Grant No.2020-I2M-C&T-B-072).
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Ren, Y., Liu, X., Pan, L. et al. Adaptive Hypersphere Data Description for few-shot one-class classification. Appl Intell 54, 12885–12897 (2024). https://doi.org/10.1007/s10489-024-05836-w
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DOI: https://doi.org/10.1007/s10489-024-05836-w