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In order to learn better about how different visual recognition backbonesmake decisions, we propose a methodology that systematically applies deepexplanation algorithms on a dataset-wide basis, and compares the statisticsgenerated from the amount and nature of the explanations to gain insights aboutthe decision-making of different models. Specifically, we propose twomethodologies called sub-explanation counting and cross-testing. Thesemethodologies reveal the difference among networks in terms of two propertiescalled compositionality and disjunctivism. Transformers and ConvNeXt are foundto be more compositional, in the sense that they jointly consider multipleparts of the image in building their decisions, whereas traditional CNNs anddistilled transformers are less compositional and more disjunctive, which meansthat they use multiple diverse but smaller set of parts to achieve a confidentprediction. Through further experiments, we pinpointed the choice ofnormalization to be especially important in the compositionality of a model, inthat batch normalization leads to less compositionality while group and layernormalization lead to more. Finally, we also analyze the features shared bydifferent backbones and plot a landscape of different models based on theirfeature-use similarity.

Comparing the Decision-Making Mechanisms by Transformers and CNNs via Explanation Methods