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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Sebastian Bach1, Alexander Binder2, Grégoire Montavon3
1Machine Learning Group, Fraunhofer Heinrich Hertz Institute, Berlin, Germany; Machine Learning Group, Technische Universität Berlin, Berlin, Germany.
Plos One
|July 11, 2015
Summary
This study introduces a pixel-wise decomposition method to interpret automated image classification. Visualizing pixel contributions as heatmaps helps experts verify decisions and focus analysis on key image regions.
Area of Science:
- Computer Vision
- Machine Learning Interpretability
- Artificial Intelligence
Background:
- Automated image classification systems are valuable but often act as "black boxes", hindering understanding of their decision-making processes.
- Interpreting these decisions is crucial for verifying system reasoning and providing insights to human experts in various applications.
Purpose of the Study:
- To develop a general solution for understanding classification decisions in automated image classification systems.
- To enable visualization of individual pixel contributions to a classifier's prediction.
Main Methods:
- Proposes a pixel-wise decomposition methodology for nonlinear classifiers.
- Visualizes pixel contributions as heatmaps for kernel-based classifiers (Bag of Words features) and multilayered neural networks.
- Evaluates the method on diverse datasets including PASCAL VOC 2009, synthetic data, MNIST, and a pre-trained ImageNet model (Caffe).
Main Results:
- The developed methodology successfully visualizes pixel contributions to classification predictions.
- Heatmap visualizations allow human experts to intuitively verify classification validity.
- Identified regions of interest within images for further expert analysis.
Conclusions:
- The proposed pixel-wise decomposition offers a general approach to interpreting automated image classification decisions.
- Visualizing pixel contributions enhances transparency and aids expert verification, improving trust and utility of AI systems in image analysis.
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