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Group visualization of class-discriminative features
Rui Shi1, Tianxing Li1, Yasushi Yamaguchi1
1Department of General Systems Studies, The University of Tokyo, Tokyo, Japan.
Summary
This study introduces class-discriminative feature groups to explain convolutional neural network (CNN) behavior. The new method visualizes how CNNs extract image features, aiding in debugging and understanding network decisions.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Explaining convolutional neural network (CNN) behavior is crucial for trust and debugging.
- Existing visualization methods often lack clear correlations between network outputs and extracted features.
Purpose of the Study:
- To define and detect "class-discriminative feature groups" in CNNs.
- To develop a visualization method for interpreting these feature groups and their relation to image regions and network predictions.
Main Methods:
- Defined "class-discriminative feature groups" based on correlated convolutional kernels and image classes.
- Proposed a detection method for these feature groups.
- Developed a visualization technique to highlight relevant image regions and interpret feature groups.
Main Results:
- Demonstrated the ability to disentangle features based on image classes.
- Showcased visualization of feature groups from specific image regions.
- Successfully applied the method to analyze adversarial samples and identify features of non-class objects.
Conclusions:
- The proposed method offers intuitive interpretation of CNN feature extraction.
- It effectively visualizes class-specific features and aids in debugging network failures.
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