Book Image

Deep Learning and XAI Techniques for Anomaly Detection

By : Cher Simon
Book Image

Deep Learning and XAI Techniques for Anomaly Detection

By: Cher Simon

Overview of this book

Despite promising advances, the opaque nature of deep learning models makes it difficult to interpret them, which is a drawback in terms of their practical deployment and regulatory compliance. Deep Learning and XAI Techniques for Anomaly Detection shows you state-of-the-art methods that’ll help you to understand and address these challenges. By leveraging the Explainable AI (XAI) and deep learning techniques described in this book, you’ll discover how to successfully extract business-critical insights while ensuring fair and ethical analysis. This practical guide will provide you with tools and best practices to achieve transparency and interpretability with deep learning models, ultimately establishing trust in your anomaly detection applications. Throughout the chapters, you’ll get equipped with XAI and anomaly detection knowledge that’ll enable you to embark on a series of real-world projects. Whether you are building computer vision, natural language processing, or time series models, you’ll learn how to quantify and assess their explainability. By the end of this deep learning book, you’ll be able to build a variety of deep learning XAI models and perform validation to assess their explainability.

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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
1
Part 1 – Introduction to Explainable Deep Learning Anomaly Detection
Part 1 – Introduction to Explainable Deep Learning Anomaly Detection
Free Chapter
2
Chapter 1: Understanding Deep Learning Anomaly Detection
Chapter 1: Understanding Deep Learning Anomaly Detection
Technical requirements
Exploring types of anomalies
Discovering real-world use cases
Considering when to use deep learning and what for
Understanding challenges and opportunities
Summary
3
Chapter 2: Understanding Explainable AI
Chapter 2: Understanding Explainable AI
Understanding the basics of XAI
Reviewing XAI significance
Choosing XAI techniques
Summary
4
Part 2 – Building an Explainable Deep Learning Anomaly Detector
Part 2 – Building an Explainable Deep Learning Anomaly Detector
5
Chapter 3: Natural Language Processing Anomaly Explainability
Chapter 3: Natural Language Processing Anomaly Explainability
Technical requirements
Understanding natural language processing
Problem
Solution walk-through
Exercise
6
Chapter 4: Time Series Anomaly Explainability
Chapter 4: Time Series Anomaly Explainability
Understanding time series
Understanding explainable deep anomaly detection for time series
Technical requirements
The problem
Solution walkthrough
Exercise
Summary
7
Chapter 5: Computer Vision Anomaly Explainability
Chapter 5: Computer Vision Anomaly Explainability
Reviewing visual anomaly detection
Integrating deep visual anomaly detection with XAI
Technical requirements
Problem
Solution walkthrough
Exercise
Summary
8
Part 3 – Evaluating an Explainable Deep Learning Anomaly Detector
Part 3 – Evaluating an Explainable Deep Learning Anomaly Detector
9
Chapter 6: Differentiating Intrinsic and Post Hoc Explainability
Chapter 6: Differentiating Intrinsic and Post Hoc Explainability
Technical requirements
Understanding intrinsic explainability
Understanding post hoc explainability
Considering intrinsic versus post hoc explainability
Summary
10
Chapter 7: Backpropagation versus Perturbation Explainability
Chapter 7: Backpropagation versus Perturbation Explainability
Reviewing backpropagation explainability
Reviewing perturbation explainability
Comparing backpropagation and perturbation XAI
Summary
11
Chapter 8: Model-Agnostic versus Model-Specific Explainability
Chapter 8: Model-Agnostic versus Model-Specific Explainability
Technical requirements
Reviewing model-agnostic explainability
Reviewing model-specific explainability
Choosing an XAI method
Summary
12
Chapter 9: Explainability Evaluation Schemes
Chapter 9: Explainability Evaluation Schemes
Reviewing the System Causability Scale (SCS)
Exploring Benchmarking Attribution Methods (BAM)
Understanding faithfulness and monotonicity
Human-grounded evaluation framework
Summary
13
Index
Index
Why subscribe?
14
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To get the most out of this book

You will need a Jupyter environment with Python 3.8+ to run the example walk-throughs in this book. Each sample notebook comes with a requirement.txt file that lists the package dependencies. You can experiment with the sample notebooks on Amazon SageMaker Studio Lab (https://aws.amazon.com/sagemaker/studio-lab/). This free ML development environment provides up to 12 hours of CPU or 4 hours of GPU per user session and 15 GiB storage at no cost.

Software/hardware covered in the book

Operating system requirements

Python 3.8+

Windows, macOS, or Linux

TensorFlow 2.11+

Windows, macOS, or Linux

AutoGluon 0.6.1+

Windows, macOS, or Linux

Cleanlab 2.2.0+

Windows, macOS, or Linux

A valid email address is all you need to get started with Amazon SageMaker Studio Lab. You do not need to configure infrastructure, manage identity and access, or even sign up for an AWS account. For more information, please refer to https://docs.aws.amazon.com/sagemaker/latest/dg/studio-lab-overview.html. Alternatively, you can try the practical examples on your preferred Integrated Development Environment (IDE).

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

A basic understanding of deep learning and anomaly detection-related topics using Python is recommended. Each chapter comes with example walk-throughs that help you gain hands-on experience, except for Chapters 2 and 9, which focus more on conceptual discussions. We suggest running the provided sample notebooks while reading a specific chapter. Additional exercises are available in Chapters 3, 4, and 5 to reinforce your learning.

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