14 citations · 41 across the 37 of their papers we have counts for
7 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…
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…
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…
Continual Learning Approaches for Anomaly Detection
Davide Dalle Pezze, Eugenia Anello, Chiara Masiero +1
Anomaly Detection is a relevant problem that arises in numerous real-world applications, especially when dealing with images. However, there has been little research for this task…
On the Properties of Adversarially-Trained CNNs
Mattia Carletti, Matteo Terzi, Gian Antonio Susto
Adversarial Training has proved to be an effective training paradigm to enforce robustness against adversarial examples in modern neural network architectures. Despite many efforts…
Improving Robustness with Image Filtering
Matteo Terzi, Mattia Carletti, Gian Antonio Susto
Adversarial robustness is one of the most challenging problems in Deep Learning and Computer Vision research. All the state-of-the-art techniques require a time-consuming procedure…