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Found 6 papers, 3 papers with code
Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD
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 challenge exacerbated in the evolving Industry 5. 0 with its human-centric focus.
AcME-AD: Accelerated Model Explanations for Anomaly Detection
Pursuing fast and robust interpretability in Anomaly Detection is crucial, especially due to its significance in practical applications.
ExIFFI and EIF+: Interpretability and Enhanced Generalizability to Extend the Extended Isolation Forest
The analysis demonstrates the effectiveness of ExIFFI in providing explanations for AD predictions.
Continual Learning Approaches for Anomaly Detection
The proposed technique scales and compresses the original images using a Super Resolution model which, to the best of our knowledge, is studied for the first time in the Continual Learning setting.
A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring
For the first time, we study multi-label classification in the Domain Incremental Learning scenario.
AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Box
In the context of human-in-the-loop Machine Learning applications, like Decision Support Systems, interpretability approaches should provide actionable insights without making the users wait.
