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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.

Neural Networks : the Official Journal of the International Neural Network Society
|June 6, 2020
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
Convolutional neural networksFeature visualizationMatrix decompositionShapley values

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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.