  As the use of deep neural networks continues to grow, understanding their
behaviour has become more crucial than ever. Post-hoc explainability methods
are a potential solution, but their reliability is being called into question.
Our research investigates the response of post-hoc visual explanations to
naturally occurring transformations, often referred to as augmentations. We
anticipate explanations to be invariant under certain transformations, such as
changes to the colour map while responding in an equivariant manner to
transformations like translation, object scaling, and rotation. We have found
remarkable differences in robustness depending on the type of transformation,
with some explainability methods (such as LRP composites and Guided Backprop)
being more stable than others. We also explore the role of training with data
augmentation. We provide evidence that explanations are typically less robust
to augmentation than classification performance, regardless of whether data
augmentation is used in training or not.
