  The promising performances of CNNs often overshadow the need to examine
whether they are doing in the way we are actually interested. We show through
experiments that even over-parameterized models would still solve a dataset by
recklessly leveraging spurious correlations, or so-called 'shortcuts'. To
combat with this unintended propensity, we borrow the idea of printer test page
and propose a novel approach called White Paper Assistance. Our proposed method
involves the white paper to detect the extent to which the model has preference
for certain characterized patterns and alleviates it by forcing the model to
make a random guess on the white paper. We show the consistent accuracy
improvements that are manifest in various architectures, datasets and
combinations with other techniques. Experiments have also demonstrated the
versatility of our approach on fine-grained recognition, imbalanced
classification and robustness to corruptions.
