Why the Failure? How Adversarial Examples Can Provide Insights for Interpretable Machine Learning

Pages: 838 - 845
Published: Jul 10, 2018
Abstract
Recent advances in Machine Learning (ML) have profoundly changed many detection, classification, recognition and inference tasks. Given the complexity of the battlespace, ML has the potential to revolutionise how Coalition Situation Understanding is synthesised and revised. However, many issues must be overcome before its widespread adoption. In this paper we consider two - interpretability and adversarial attacks. Interpretability is needed...
Paper Details
Title
Why the Failure? How Adversarial Examples Can Provide Insights for Interpretable Machine Learning
Published Date
Jul 10, 2018
Journal
Pages
838 - 845
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