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Check out our latest research on adversarial robustness and generalization of deep networks.

TAG»ADVERSARIAL MACHINE LEARNING«

ARTICLE

Towards a Definition for Adversarial Examples

Obtaining deep networks robust against adversarial examples is a widely open problem. While many papers are devoted to training more robust deep networks, a clear definition of adversarial examples has not been agreed upon. In this article, I want to discuss two very simple toy examples illustrating the necessity of a proper definition of adversarial examples.

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04thDECEMBER2018

PROJECT

Disentangling the relationship between adversarial robustness and generalization.

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ARTICLE

ArXiv Pre-Print “Disentangling Adversarial Robustness and Generalization”

To date, it is unclear whether we can obtain both accurate and robust deep networks — meaning deep networks that generalize well and resist adversarial examples. In this pre-print, we aim to disentangle the relationship between adversarial robustness and generalization. The paper is available on ArXiv.

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