Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation
Distribution shift is a major source of failure for machine learning models.
However, evaluating model reliability under distribution shift can be
challenging, especially since it may be difficult to acquire counterfactual
examples that exhibit a specified shift. In this work, we introduce the notion
of a dataset interface: a framework that, given an input dataset and a
user-specified shift, returns instances from that input distribution that
exhibit the desired shift. We study a number of natural implementations for
such an interface, and find that they often introduce confounding shifts that
complicate model evaluation. Motivated by this, we propose a dataset interface
implementation that leverages Textual Inversion to tailor generation to the
input distribution. We then demonstrate how applying this dataset interface to
the ImageNet dataset enables studying model behavior across a diverse array of
distribution shifts, including variations in background, lighting, and
attributes of the objects. Code available at
https://github.com/MadryLab/dataset-interfaces.