# Improving Data Quality through Deep Learning and Statistical Models

arXiv: https://arxiv.org/abs/1810.07132 · HTML: https://mlsanity.com/paper/1810.07132/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2018.

## Built on by (most cited first, 6 of 6)

- [Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation](https://mlsanity.com/paper/2103.03102/) (2021)
- [Statistical Post-Processing for Gridded Temperature Prediction Using Encoder-Decoder-Based Deep Convolutional Neural Networks](https://mlsanity.com/paper/2103.01479/) (2021)
- [Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation](https://mlsanity.com/paper/2203.01323/) (2022)
- [Research information in the light of artificial intelligence: quality and data ecologies](https://mlsanity.com/paper/2405.12997/) (2024)
- [Towards Explainable Automated Data Quality Enhancement without Domain Knowledge](https://mlsanity.com/paper/2409.10139/) (2024)
- [An Agentic Retrieval Framework for Autonomous Context-Aware Data Quality Assessment](https://mlsanity.com/paper/2606.13692/) (2026)

## Abstract (arXiv)

Traditional data quality control methods are based on users experience or previously established business rules, and this limits performance in addition to being a very time consuming process with lower than desirable accuracy. Utilizing deep learning, we can leverage computing resources and advanced techniques to overcome these challenges and provide greater value to users. In this paper, we, the authors, first review relevant works and discuss machine learning techniques, tools, and statistical quality models. Second, we offer a creative data quality framework based on deep learning and statistical model algorithm for identifying data quality. Third, we use data involving salary levels from an open dataset published by the state of Arkansas to demonstrate how to identify outlier data and how to improve data quality via deep learning. Finally, we discuss future work.