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Deep Learning and XAI Techniques for Anomaly Detection

You're reading from Deep Learning and XAI Techniques for Anomaly Detection

Product type Book

Published in Jan 2023

Publisher Packt

ISBN-13 9781804617755

Pages 218 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Cher Simon

Table of Contents (15) Chapters

Preface

1. Part 1 – Introduction to Explainable Deep Learning Anomaly Detection

2. Chapter 1: Understanding Deep Learning Anomaly Detection

3. Chapter 2: Understanding Explainable AI

4. Part 2 – Building an Explainable Deep Learning Anomaly Detector

5. Chapter 3: Natural Language Processing Anomaly Explainability

6. Chapter 4: Time Series Anomaly Explainability

7. Chapter 5: Computer Vision Anomaly Explainability

8. Part 3 – Evaluating an Explainable Deep Learning Anomaly Detector

9. Chapter 6: Differentiating Intrinsic and Post Hoc Explainability

10. Chapter 7: Backpropagation versus Perturbation Explainability

11. Chapter 8: Model-Agnostic versus Model-Specific Explainability

12. Chapter 9: Explainability Evaluation Schemes

13. Index

Why subscribe?

14. Other Books You May Enjoy

Summary

This chapter showed you how CAM and Grad-CAM provide anomaly localization by identifying class discriminative activation maps. In addition, you learned how to provide transparency and build user trust by adding visual explanations into a CNN for computer vision anomaly detection. In the next chapter, we will explore intrinsic versus post hoc explainability.

The rest of the chapter is locked

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Authors (1)

Cher Simon

Cher Simon is a principal solutions architect specializing in artificial intelligence, machine learning, and data analytics at AWS. Cher has 20 years of experience in architecting enterprise-scale, data-driven, and AI-powered industry solutions. Besides building cloud-native solutions in her day-to-day role with customers, Cher is also an avid writer and a frequent speaker at AWS conferences.

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