Image Anomaly Detection with Capsule Networks and Imbalanced Datasets
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Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687 · see citing works
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Xiao, H., Rasul, K., & Vollgraf, R. (2017) · arXiv · doi:10.48550/arxiv.1708.07747 · Cited by 6,108 · see citing works
Dynamic Routing Between Capsules
Sabour, S., Frosst, N., & Hinton, G. E. (2017) · arXiv · doi:10.48550/arxiv.1710.09829 · Cited by 4,167 · see citing works
A systematic study of the class imbalance problem in convolutional neural networks
Buda, M., Maki, A., & Mazurowski, M. A. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.07.011 · Cited by 3,088 · cited 1× · see citing works
“detection with capsule networks and imbalanced datasets 3 of new data. In both cases the idea is to use a pre-processing step to make the dataset balanced before applying any classification algorithm [4]. Regardingsemi-supervised orunsupervisedapproaches,earlyworksadopted techniques such as Deep Belief Networks [20] f…”
MethodUnsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_12 · Cited by 2,627 · see citing works
Ruff, L., Vandermeulen, R. A., Görnitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Müller, E., & Kloft, M. (2018) · International Conference on Machine Learning · Cited by 1,719 · see citing works
A survey of network anomaly detection techniques
Ahmed, M., Mahmood, A., & Hu, J.-K. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2015.11.016 · Cited by 1,708 · see citing works
Anomaly Detection with Robust Deep Autoencoders
Zhou, C., & Paffenroth, R. C. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098052 · Cited by 1,548 · see citing works
Deep Anomaly Detection with Outlier Exposure
Hendrycks, D., Mazeika, M., & Dietterich, T. (2018) · arXiv · doi:10.48550/arxiv.1812.04606 · Cited by 932 · see citing works
GANomaly: Semi-supervised Anomaly Detection via Adversarial Training
Akçay, S., Atapour–Abarghouei, A., & Breckon, T. P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20893-6_39 · Cited by 613 · see citing works
Trajectory-Based Anomalous Event Detection
Piciarelli, C., Micheloni, C., & Foresti, G. L. (2008) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2008.2005599 · Cited by 532 · see citing works
Self-citationNetwork anomaly detection with the restricted Boltzmann machine
Fiore, U., Palmieri, F., Castiglione, A., & De Santis, A. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.11.050 · Cited by 376 · see citing works
Automated visual inspection in the semiconductor industry: A survey
Huang, S.-H., & Pan, Y.-C. (2015) · Computers in Industry · doi:10.1016/j.compind.2014.10.006 · Cited by 307 · see citing works
Application of data mining techniques for medical image classification
Antonie, M.-L., Zai͏̈ane, O. R., & Coman, A. (2001) · Cited by 306 · see citing works
Image Anomaly Detection with Generative Adversarial Networks
Deecke, L., Vandermeulen, R. A., Ruff, L., Mandt, S., & Kloft, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10925-7_1 · Cited by 222 · see citing works
Robust, Deep and Inductive Anomaly Detection
Chalapathy, R., Menon, A. K., & Chawla, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71249-9_3 · Cited by 167 · see citing works
Distributed Real-Time Anomaly Detection in Networked Industrial Sensing Systems
Chen, P.-Y., Yang, S., & McCann, J. A. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2350451 · Cited by 148 · see citing works
Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets
Wulsin, D., Blanco, J., Mani, R., & Litt, B. (2010) · 2010 Ninth International Conference on Machine Learning and Applications · doi:10.1109/icmla.2010.71 · Cited by 98 · cited 1× · see citing works
“cessing step to make the dataset balanced before applying any classification algorithm [4]. Regardingsemi-supervised orunsupervisedapproaches,earlyworksadopted techniques such as Deep Belief Networks [20] for medical diagnosis on EEG waveforms or Restricted Boltzmann Machines [11] for network traffic analysis. Morerec…”
MethodA Vision-Based System for Internal Pipeline Inspection
Piciarelli, C., Avola, D., Pannone, D., & Foresti, G. L. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873237 · Cited by 95 · see citing works
Self-citationGroup Anomaly Detection Using Deep Generative Models
Chalapathy, R., Toth, E., & Chawla, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10925-7_11 · Cited by 60 · see citing works
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2 of the 23 references couldn’t be matched to a known paper.
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Sorted by citations
VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization
Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., & Foresti, G. L. (2021) · 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie45552.2021.9576231 · Cited by 462 · see citing works
“This requires specialized approaches to handle data imbalance [22], [23].”
BackgroundSelf-citationMOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images
Zimmerer, D., Full, P. M., Isensee, F., Jäger, P. F., Adler, T. J., Petersen, J., Koehler, G., Roß, T., Reinke, A., Kascenas, A., et al. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3170077 · Cited by 68 · see citing works
Image-Based Feature Representation for Insider Threat Classification
Gayathri, R. G., Sajjanhar, A., & Xiang, Y. (2020) · Applied Sciences · doi:10.3390/app10144945 · Cited by 63 · see citing works
Randive, K., Mohan, R., & Sivakrishna, A. M. (2023) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103434 · Cited by 27 · see citing works
Neuro-evolutionary models for imbalanced classification problems
Albadarneh, I., Habib, M., Aljarah, I., & Faris, H. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.11.005 · Cited by 24 · see citing works
Image Anomaly Detection by Aggregating Deep Pyramidal Representations
Mishra, P. K., Piciarelli, C., & Foresti, G. L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68799-1_51 · Cited by 15 · see citing works
Self-citationSupervised Anomaly Detection with Highly Imbalanced Datasets Using Capsule Networks
Piciarelli, C., Mishra, P. K., & Foresti, G. L. (2021) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001421520108 · Cited by 6 · see citing works
Self-citationWu, X., Chen, H., Wu, X., Wu, S., & Huang, J. (2020) · Soft Computing · doi:10.1007/s00500-020-05067-4 · Cited by 4 · see citing works
RetractedMeasuring internal inequality in capsule networks for supervised anomaly detection
Kirillov, B., & Panov, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-17734-7 · Cited by 4 · see citing works
Anomaly detection based capsnet for malicious Url detection system
Alsowail, R. A. (2025) · Wireless Networks · doi:10.1007/s11276-025-03960-0 · Cited by 2 · see citing works
- Authors
- Piciarelli, C., Mishra, P., & Foresti, G. L.
- Affiliations
- University of Udine (IT)
- Corresponding author
- Piciarelli, Claudio
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 257-267
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2971501370
- Cited by
- 10
- References
- 23
- Citations by year
- 2025: 1 · 2023: 1 · 2022: 2 · 2021: 3 · 2020: 3
- FWCI
- 0.77
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Institutional Research Information System (University of Udine) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- image anomaly detection, capsule networks, class imbalance, anomaly detection, deep learning, industrial inspection, diagnostic imaging, security enforcement
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