14 citations · 41 across the 37 of their papers we have counts for
12 papers · 1 filter
PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge
Manuel Barusco, Francesco Borsatti, Davide Dalle Pezze +3
Visual Anomaly Detection (VAD) has gained significant research attention for its ability to identify anomalous images and pinpoint the specific areas responsible for the anomaly. A…
TiROD: Tiny Robotics Dataset and Benchmark for Continual Object Detection
Francesco Pasti, Riccardo De Monte, Davide Dalle Pezze +2
Detecting objects with visual sensors is crucial for numerous mobile robotics applications, from autonomous navigation to inspection. However, robots often need to operate under si…
Replay Consolidation with Label Propagation for Continual Object Detection
Riccardo De Monte, Davide Dalle Pezze, Marina Ceccon +5
Continual Learning (CL) aims to learn new data while remembering previously acquired knowledge. In contrast to CL for image classification, CL for Object Detection faces additional…
Fairness Evolution in Continual Learning for Medical Imaging
Marina Ceccon, Davide Dalle Pezze, Alessandro Fabris +1
Deep Learning has advanced significantly in medical applications, aiding disease diagnosis in Chest X-ray images. However, expanding model capabilities with new data remains a chal…
A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learning
Matteo Cederle, Marco Fabris, Gian Antonio Susto
Autonomous intersection management (AIM) poses significant challenges due to the intricate nature of real-world traffic scenarios and the need for a highly expensive centralised se…
Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI
Davide Frizzo, Francesco Borsatti, Alessio Arcudi +3
Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomal…
Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD
Valentina Zaccaria, Chiara Masiero, David Dandolo +1
While Machine Learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challeng…
Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark
Marina Ceccon, Davide Dalle Pezze, Alessandro Fabris +1
Despite the critical importance of the medical domain in Deep Learning, most of the research in this area solely focuses on training models in static environments. It is only in re…
Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning
Nikola Bugarin, Jovana Bugaric, Manuel Barusco +2
Anomaly Detection is a relevant problem in numerous real-world applications, especially when dealing with images. However, little attention has been paid to the issue of changes ov…
AcME-AD: Accelerated Model Explanations for Anomaly Detection
Valentina Zaccaria, David Dandolo, Chiara Masiero +1
Pursuing fast and robust interpretability in Anomaly Detection is crucial, especially due to its significance in practical applications. Traditional Anomaly Detection methods excel…
Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
Luca Della Libera, Jacopo Andreoli, Davide Dalle Pezze +2
A crucial task in predictive maintenance is estimating the remaining useful life of physical systems. In the last decade, deep learning has improved considerably upon traditional m…
Improving robustness of quantum feedback control with reinforcement learning
Manuel Guatto, Gian Antonio Susto, Francesco Ticozzi
Obtaining reliable state preparation protocols is a key step towards practical implementation of many quantum technologies, and one of the main tasks in quantum control. In this wo…