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With the unprecedented performance achieved by deep learning, it is commonly believed that deep neural networks (DNNs) attempt to extract informative features for learning tasks. To formalize this intuition, we apply the local information geometric analysis and establish an information-theoretic framework for feature selection, which demonstrates the information-theoretic optimality of DNN features. Moreover, we conduct a quantitative analysis to characterize the impact of network structure on the feature extraction process of DNNs. Our investigation naturally leads to a performance metric for evaluating the effectiveness of extracted features, called the H-score, which illustrates the connection between the practical training process of DNNs and the information-theoretic framework. Finally, we validate our theoretical results by experimental designs on synthesized data and the ImageNet dataset.
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Symbolic Execution for Importance Analysis and Adversarial Generation in Neural Networks
Deep Neural Networks (DNN) are increasingly used in a variety of applications, many of them with serious safety and security concerns. This paper describes DeepCheck, a new approach for validating DNNs based on core ideas from program analysis, specifically from symbolic execution. DeepCheck implements novel techniques for lightweight symbolic analysis of DNNs and applies them to address two challenging problems in DNN analysis: 1) identification of important input features and 2) leveraging those features to create adversarial inputs. Experimental results with an MNIST image classification network and a sentiment network for textual data show that DeepCheck promises to be a valuable tool for DNN analysis.
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- PAR ID:
- 10190173
- Date Published:
- Journal Name:
- IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)
- Page Range / eLocation ID:
- 313 to 322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISSRE.2019.00039
