Revisiting the Relation Between Robustness and Universality

Authors: Laura Caspari, Max Klabunde, Florian Lemmerich
Primary area: interpretability and explainable AI
Keywords: similarity, representational similarity, functional similarity, adversarial robustness, universality

Abstract:
The *modified universality hypothesis* proposed by Jones et al. (2022) suggests that adversarially robust models trained for a given task are highly similar. We revisit the hypothesis and test its generality. We find that predictive behavior does not converge with increasing robustness and thus is not universal. Further, with additional similarity measures, we uncover differences in the representations that were invisible with the measures used in prior work. While robust models tend to be more similar than standard models, robust models remain distinct in important aspects. Moreover, the importance of similarity measures when comparing representations is highlighted as the absolute level of similarity---and thus the assessment of universality---is heavily dependent on the measure used.

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