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Data Sanity Check for Deep Learning Systems via Learnt Assertions
[article]
2019
arXiv
pre-print
Reliability is a critical consideration to DL-based systems. But the statistical nature of DL makes it quite vulnerable to invalid inputs, i.e., those cases that are not considered in the training phase of a DL model. This paper proposes to perform data sanity check to identify invalid inputs, so as to enhance the reliability of DL-based systems. We design and implement a tool to detect behavior deviation of a DL model when processing an input case. This tool extracts the data flow footprints
arXiv:1909.03835v3
fatcat:5e2pvqaquvalvgoko4e36f7jqq