Published in Digital Signal Processing, 2025, 2025
This paper investigates the adoption of MaskRCNN segmentation model to scale the task of Anomaly Detection over industrial product images to igher resolutions
Recommended citation: Samele, S., Attore, F., Matteucci, M. (2025). "Scaling anomaly detection with segmentation models." 2025 Digital Signal Processing 105613.
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Published in 2024 International Joint Conference on Neural Networks (IJCNN), 2024
This paper explores the potential of scaling anomaly detection to high resolution images using self-supervised Vision Transformers
Recommended citation: Samele, S., Matteucci, M. (2024). "Self-Supervised Vision Transformers for Scalable Anomaly Detection over Images." 2024 International Joint Conference on Neural Networks (IJCNN). pp. 1–10.
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Published in 5th International Conference on Artificial Intelligence and Pattern Recognition, 2022
This work proposes SGDE, a privacy-preserving data exchange technique for federated learning across silos.
Recommended citation: Lomurno, E., Archetti, A., Cazzella, L., Samele, S., Di Perna, L., Matteucci, M. (2022). "SGDE: Secure generative data exchange for cross-silo federated learning." Proceedings of the 5th International Conference on Artificial Intelligence and Pattern Recognition. pp. 205–214.
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Published in 2022 26th International Conference on Pattern Recognition (ICPR), 2022
This method detects image anomalies using sparse dictionary learning on activation maps of pre-trained neural networks.
Recommended citation: Samele, S., Matteucci, M. (2022). "Patchwise Sparse Dictionary Learning from pre-trained Neural Network Activation Maps for Anomaly Detection in Images." 2022 26th International Conference on Pattern Recognition (ICPR). pp. 1307–1313.
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Published in 2022 International Joint Conference on Neural Networks (IJCNN), 2022
PopNASv2 improves neural architecture search with a multi-objective perspective focusing on efficiency and performance.
Recommended citation: Falanti, A., Lomurno, E., Samele, S., Ardagna, D., Matteucci, M. (2022). "Popnasv2: An efficient multi-objective neural architecture search technique." 2022 International Joint Conference on Neural Networks (IJCNN). pp. 1–8.
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Published in 2022 International Joint Conference on Neural Networks (IJCNN), 2022
We introduce a defense method against adversarial attacks using massive image inpainting, showing strong robustness.
Recommended citation: Ughini, G., Samele, S., Matteucci, M. (2022). "Trust-no-pixel: A remarkably simple defense against adversarial attacks based on massive inpainting." 2022 International Joint Conference on Neural Networks (IJCNN). pp. 1–10.
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Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021
This paper presents a Pareto-optimal approach to progressive neural architecture search, balancing multiple objectives.
Recommended citation: Lomurno, E., Samele, S., Matteucci, M., Ardagna, D. (2021). "Pareto-optimal progressive neural architecture search." Proceedings of the Genetic and Evolutionary Computation Conference Companion. pp. 1726–1734.
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