  Saliency methods are a popular class of feature attribution explanation
methods that aim to capture a model's predictive reasoning by identifying
"important" pixels in an input image. However, the development and adoption of
these methods are hindered by the lack of access to ground-truth model
reasoning, which prevents accurate evaluation. In this work, we design a
synthetic benchmarking framework, SMERF, that allows us to perform
ground-truth-based evaluation while controlling the complexity of the model's
reasoning. Experimentally, SMERF reveals significant limitations in existing
saliency methods and, as a result, represents a useful tool for the development
of new saliency methods.
