Visualizing Classification Structure of Large-Scale Classifiers

Abstract

We propose a measure to compute class similarity in large-scale classification based on prediction scores. Such measure has not been formally pro-posed in the literature. We show how visualizing the class similarity matrix can reveal hierarchical structures and relationships that govern the classes. Through examples with various classifiers, we demonstrate how such structures can help in analyzing the classification behavior and in inferring potential corner cases. The source code for one example is available as a notebook at https://github.com/bilalsal/blocks

arXiv comment2020 ICML Workshop on Human Interpretability in Machine Learning (WHI 2020)

References in corpus (7)

• Distilling the Knowledge in a Neural Network
• Learning Important Features Through Propagating Activation Differences
• SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability
• Do Convolutional Neural Networks Learn Class Hierarchy?
• Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks
• Convergent Learning: Do different neural networks learn the same representations?
• Prediction Scores as a Window into Classifier Behavior