Deep Learning and XAI Techniques for Anomaly Detection

Part 2 – Building an Explainable Deep Learning Anomaly Detector

Part 2 dives into building deep learning anomaly detectors in three major application domains using various data modalities, with step-by-step example walk-throughs. By the end of Part 2, you will have learned how to quickly develop sophisticated anomaly detectors using state-of-the-art frameworks such as AutoGluon and Cleanlab, and be able to apply XAI techniques to extend the model’s explainability in these domains.

This part comprises the following chapters:

• Chapter 3, Natural Language Processing Anomaly Explainability
• Chapter 4, Time Series Anomaly Explainability
• Chapter 5, Computer Vision Anomaly Explainability