  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.
