1 citations · 2 across the 17 of their papers we have counts for
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Function Based Isolation Forest (FuBIF): A Unifying Framework for Interpretable Isolation-Based Anomaly Detection
Alessio Arcudi, Alessandro Ferreri, Francesco Borsatti +1
Anomaly Detection (AD) is evolving through algorithms capable of identifying outliers in complex datasets. The Isolation Forest (IF), a pivotal AD technique, exhibits adaptability…
Explainable Visual Anomaly Detection via Concept Bottleneck Models
Arianna Stropeni, Valentina Zaccaria, Francesco Borsatti +3
In recent years, VAD (Visual Anomaly Detection) has attracted significant attention due to its high interpretability, as it provides visual explanations by highlighting anomalous r…
Towards Continual Visual Anomaly Detection in the Medical Domain
Manuel Barusco, Francesco Borsatti, Nicola Beda +2
Visual Anomaly Detection (VAD) seeks to identify abnormal images and precisely localize the corresponding anomalous regions, relying solely on normal data during training. This app…
MoViAD: A Modular Library for Visual Anomaly Detection
Manuel Barusco, Francesco Borsatti, Arianna Stropeni +2
VAD is a critical field in machine learning focused on identifying deviations from normal patterns in images, often challenged by the scarcity of anomalous data and the need for un…
A Quantile Regression Approach for Remaining Useful Life Estimation with State Space Models
Davide Frizzo, Francesco Borsatti, Gian Antonio Susto
Predictive Maintenance (PdM) is pivotal in Industry 4.0 and 5.0, proactively enhancing efficiency through accurate equipment Remaining Useful Life (RUL) prediction, thus optimizing…
Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study
Manuel Barusco, Francesco Borsatti, Youssef Ben Khalifa +2
Semiconductor manufacturing is a complex, multistage process. Automated visual inspection of Scanning Electron Microscope (SEM) images is indispensable for minimizing equipment dow…
Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression
Arianna Stropeni, Francesco Borsatti, Manuel Barusco +3
Visual Anomaly Detection (VAD) is a key task in industrial settings, where minimizing operational costs is essential. Deploying deep learning models within Internet of Things (IoT)…
Memory Efficient Continual Learning for Edge-Based Visual Anomaly Detection
Manuel Barusco, Lorenzo D'Antoni, Davide Dalle Pezze +2
Visual Anomaly Detection (VAD) is a critical task in computer vision with numerous real-world applications. However, deploying these models on edge devices presents significant cha…
From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithms
Manuel Barusco, Francesco Borsatti, Davide Dalle Pezze +3
Recent advances in Visual Anomaly Detection (VAD) have introduced sophisticated algorithms leveraging embeddings generated by pre-trained feature extractors. Inspired by these deve…