Measuring Representational Robustness of Neural Networks Through Shared Invariances

• URL: http://arxiv.org/abs/2206.11939v1
• Date: Thu, 23 Jun 2022 18:49:13 GMT
• Title: Measuring Representational Robustness of Neural Networks Through Shared Invariances
• Authors: Vedant Nanda and Till Speicher and Camila Kolling and John P. Dickerson and Krishna P. Gummadi and Adrian Weller
• Abstract summary: A major challenge in studying robustness in deep learning is defining the set of meaningless'' perturbations to which a given Neural Network (NN) should be invariant. Most work on robustness implicitly uses a human as the reference model to define such perturbations. Our work offers a new view on robustness by using another reference NN to define the set of perturbations a given NN should be invariant to.
• Score: 45.94090422087775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A major challenge in studying robustness in deep learning is defining the set of ``meaningless'' perturbations to which a given Neural Network (NN) should be invariant. Most work on robustness implicitly uses a human as the reference model to define such perturbations. Our work offers a new view on robustness by using another reference NN to define the set of perturbations a given NN should be invariant to, thus generalizing the reliance on a reference ``human NN'' to any NN. This makes measuring robustness equivalent to measuring the extent to which two NNs share invariances, for which we propose a measure called STIR. STIR re-purposes existing representation similarity measures to make them suitable for measuring shared invariances. Using our measure, we are able to gain insights into how shared invariances vary with changes in weight initialization, architecture, loss functions, and training dataset. Our implementation is available at: \url{https://github.com/nvedant07/STIR}.

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