Adventures in deep learning geometry

Donald Waagen; Don Hulsey; Jamie Godwin; David Gray

doi:10.1117/12.2558596

30 April 2020 Adventures in deep learning geometry

Donald Waagen, Don Hulsey, Jamie Godwin, David Gray

Proceedings Volume 11394, Automatic Target Recognition XXX; 113940S (2020) https://doi.org/10.1117/12.2558596
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Deep learning models are pervasive for a multitude of tasks, but the complexity of these models can limit interpretation and inhibit trust. For a classification task, we investigate the induced relationships between the class conditioned data distributions, and geometrically compare/contrast the data with the deep learning models' output weight vectors. These geometric relationships are examined across models as a function of dense hidden layer width. Additionally, we geometrically characterize perturbation-based adversarial examples with respect to the deep learning model.

Citation Download Citation

Donald Waagen, Don Hulsey, Jamie Godwin, and David Gray "Adventures in deep learning geometry", Proc. SPIE 11394, Automatic Target Recognition XXX, 113940S (30 April 2020); https://doi.org/10.1117/12.2558596

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