Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples

18 Aug 2017 Yinpeng Dong Hang Su Jun Zhu Fan Bao

Deep neural networks (DNNs) have demonstrated impressive performance on a wide array of tasks, but they are usually considered opaque since internal structure and learned parameters are not interpretable. In this paper, we re-examine the internal representations of DNNs using adversarial images, which are generated by an ensemble-optimization algorithm... (read more)

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METHOD TYPE
Interpretability
Image Models