# Data Sanity Check for Deep Learning Systems via Learnt Assertions

arXiv: https://arxiv.org/abs/1909.03835 · HTML: https://mlsanity.com/paper/1909.03835/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks](https://mlsanity.com/paper/2304.02654/) (2023)

## Abstract (arXiv)

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 and conducts an assertion-based validation mechanism. The assertions are built automatically, which are specifically-tailored for DL model data flow analysis. Our experiments conducted with real-world scenarios demonstrate that such an assertion-based data sanity check mechanism is effective in identifying invalid input cases.