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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

Thu Jul 21 01:05 PM -- 01:25 PM (PDT) @ Ballroom 1 & 2

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: https://github.com/nvedant07/STIR

Author Information

Vedant Nanda (University of Maryland & MPI-SWS)
Till Speicher (MPI-SWS)
Camila Kolling (PUCRS)
John P Dickerson (Arthur AI & Univ. of Maryland)
Krishna Gummadi (MPI-SWS)
Adrian Weller (University of Cambridge, Alan Turing Institute)
Adrian Weller

Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, and is a Turing AI Fellow leading work on trustworthy Machine Learning (ML). He is a Principal Research Fellow in ML at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. Previously, Adrian held senior roles in finance. He received a PhD in computer science from Columbia University, and an undergraduate degree in mathematics from Trinity College, Cambridge.

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