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Measuring Representational Robustness of Neural Networks Through Shared Invariances

Vedant Nanda · Till Speicher · Camila Kolling · John P Dickerson · Krishna Gummadi · Adrian Weller

Hall E #427

Keywords: [ SA: Accountability, Transparency and Interpretability ] [ MISC: Representation Learning ] [ DL: Robustness ] [ DL: Other Representation Learning ]


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 referencehuman NN'' to any NN. This makes measuring robustness equivalent to measuring the extent to which two NNs share invariances.We propose a measure called \stir, which faithfully captures the extent to which two NNs share invariances. \stir re-purposes existing representation similarity measures to make them suitable for measuring shared invariances. Using our measure, we are able to gain insights about how shared invariances vary with changes in weight initialization, architecture, loss functions, and training dataset. Our implementation is available at: \url{}.

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