4 papers
Explainable Visual Anomaly Detection via Concept Bottleneck Models
Arianna Stropeni, Valentina Zaccaria, Francesco Borsatti +3
In recent years, Visual Anomaly Detection (VAD) has gained significant attention due to its ability to identify defects using only normal images during training. Many VAD models wo…
Fault Identification Enhancement with Reinforcement Learning (FIERL)
Valentina Zaccaria, Davide Sartor, Simone Del Favero +1
This letter presents a novel approach in the field of Active Fault Detection (AFD), by explicitly separating the task into two parts: Passive Fault Detection (PFD) and control inpu…
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…
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…