Towards Explanation of DNN-based Prediction with Guided Feature Inversion

19 Mar 2018Mengnan DuNinghao LiuQingquan SongXia Hu

While deep neural networks (DNN) have become an effective computational tool, the prediction results are often criticized by the lack of interpretability, which is essential in many real-world applications such as health informatics. Existing attempts based on local interpretations aim to identify relevant features contributing the most to the prediction of DNN by monitoring the neighborhood of a given input... (read more)

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