On the Adversarial Robustness of Mixture of Experts
- URL: http://arxiv.org/abs/2210.10253v1
- Date: Wed, 19 Oct 2022 02:24:57 GMT
- Title: On the Adversarial Robustness of Mixture of Experts
- Authors: Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme, Pranjal Awasthi,
Srinadh Bhojanapalli
- Abstract summary: Recently, Bubeck and Sellke proved a lower bound on the Lipschitz constant of functions that fit the training data in terms of their number of parameters.
This raises an interesting open question, do -- and can -- functions with more parameters, but not necessarily more computational cost, have better robustness?
We study this question for sparse Mixture of Expert models (MoEs) that make it possible to scale up the model size for a roughly constant computational cost.
- Score: 30.028035734576005
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Adversarial robustness is a key desirable property of neural networks. It has
been empirically shown to be affected by their sizes, with larger networks
being typically more robust. Recently, Bubeck and Sellke proved a lower bound
on the Lipschitz constant of functions that fit the training data in terms of
their number of parameters. This raises an interesting open question, do -- and
can -- functions with more parameters, but not necessarily more computational
cost, have better robustness? We study this question for sparse Mixture of
Expert models (MoEs), that make it possible to scale up the model size for a
roughly constant computational cost. We theoretically show that under certain
conditions on the routing and the structure of the data, MoEs can have
significantly smaller Lipschitz constants than their dense counterparts. The
robustness of MoEs can suffer when the highest weighted experts for an input
implement sufficiently different functions. We next empirically evaluate the
robustness of MoEs on ImageNet using adversarial attacks and show they are
indeed more robust than dense models with the same computational cost. We make
key observations showing the robustness of MoEs to the choice of experts,
highlighting the redundancy of experts in models trained in practice.
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