On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law

TL;DR

This paper critically examines the use of out-of-distribution testing in machine learning, revealing common pitfalls and demonstrating that simple methods can outperform complex ones, thus questioning the reliability of current OOD benchmarks.

Contribution

The paper identifies problematic practices in OOD testing, especially in visual question answering, and proposes solutions to improve the validity of OOD evaluations.

Findings

Simple baseline methods outperform complex models on some question types.

Current OOD benchmarks are vulnerable to overfitting and misuse.

Common practices undermine the goal of evaluating true generalization.

Abstract

Out-of-distribution (OOD) testing is increasingly popular for evaluating a machine learning system's ability to generalize beyond the biases of a training set. OOD benchmarks are designed to present a different joint distribution of data and labels between training and test time. VQA-CP has become the standard OOD benchmark for visual question answering, but we discovered three troubling practices in its current use. First, most published methods rely on explicit knowledge of the construction of the OOD splits. They often rely on ``inverting'' the distribution of labels, e.g. answering mostly 'yes' when the common training answer is 'no'. Second, the OOD test set is used for model selection. Third, a model's in-domain performance is assessed after retraining it on in-domain splits (VQA v2) that exhibit a more balanced distribution of labels. These three practices defeat the objective of evaluating generalization, and put into question the value of methods specifically designed for this dataset. We show that embarrassingly-simple methods, including one that generates answers at random, surpass the state of the art on some question types. We provide short- and long-term solutions to avoid these pitfalls and realize the benefits of OOD evaluation.