The Robustness of Deep Networks: A Geometrical Perspective

IEEE Signal Processing Magazine, pp. 50-62, 2017.

Cited by: 103|Bibtex|Views13|DOI:https://doi.org/10.1109/MSP.2017.2740965
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Abstract:

Deep neural networks have recently shown impressive classification performance on a diverse set of visual tasks. When deployed in real-world (noise-prone) environments, it is equally important that these classifiers satisfy robustness guarantees: small perturbations applied to the samples should not yield significant loss to the performan...More

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