Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example

TL;DR

Bayes-TrEx is a Bayesian sampling framework that identifies in-distribution examples with specific prediction confidences to enhance neural network interpretability, revealing model behaviors beyond standard test set analysis.

Contribution

It introduces a novel, flexible Bayesian sampling method for model inspection that finds in-distribution examples with targeted confidences, enabling comprehensive analysis of neural networks.

Findings

Reveals highly confident misclassifications and ambiguous examples

Visualizes class boundaries and extrapolation behaviors

Exposes neural network overconfidence in various datasets

Abstract

Post-hoc explanation methods are gaining popularity for interpreting, understanding, and debugging neural networks. Most analyses using such methods explain decisions in response to inputs drawn from the test set. However, the test set may have few examples that trigger some model behaviors, such as high-confidence failures or ambiguous classifications. To address these challenges, we introduce a flexible model inspection framework: Bayes-TrEx. Given a data distribution, Bayes-TrEx finds in-distribution examples with a specified prediction confidence. We demonstrate several use cases of Bayes-TrEx, including revealing highly confident (mis)classifications, visualizing class boundaries via ambiguous examples, understanding novel-class extrapolation behavior, and exposing neural network overconfidence. We use Bayes-TrEx to study classifiers trained on CLEVR, MNIST, and Fashion-MNIST, and we show that this framework enables more flexible holistic model analysis than just inspecting the test set. Code is available at https://github.com/serenabooth/Bayes-TrEx.