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作者 主題: Pattern Recognition-Special Issue on Explainable Deep Learning-20190715  (閱讀 287 次)


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eep neural networks (DNNs) have recently achieved outstanding predictive performance, and become an indispensable tool in a wide range of pattern recognition applications, including image classification, object detection, video understanding, document analysis, etc. While DNN methods give impressively high predictive accuracy, they are often perceived as black-boxes with deep, computationally expensive layers, and have been recently found vulnerable to spoofing with well-designed input samples in many safety critical applications. This is especially so in several sensitive or real-time pattern recognition applications including medical diagnosis, face recognition and self-driving cars. In these applications, a single incorrect prediction might be very costly, and thus the reliance on the trained model and its capacity to deliver both efficient and robust data processing must be guaranteed. Therefore, understanding how the DNN behaves, and thus generating explainable deep learning models have become an essential and fundamental problem.

Submission Period: June 1st - July 15th, 2019

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