  Over the past years, the crucial role of data has largely been shadowed by
the field's focus on architectures and training procedures. We often cause
changes to the data without being aware of their wider implications. In this
paper we show that distorting images without accounting for the artefacts
introduced leads to biased results when establishing occlusion robustness. To
ensure models behave as expected in real-world scenarios, we need to rule out
the impact added artefacts have on evaluation. We propose a new approach,
iOcclusion, as a fairer alternative for applications where the possible
occluders are unknown.
