# Sequential Drift Detection in Deep Learning Classifiers

arXiv: https://arxiv.org/abs/2007.16109 · HTML: https://mlsanity.com/paper/2007.16109/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Task-Sensitive Concept Drift Detector with Constraint Embedding](https://mlsanity.com/paper/2108.06980/) (2021)
- [Challenges and Solutions to Build a Data Pipeline to Identify Anomalies in Enterprise System Performance](https://mlsanity.com/paper/2112.08940/) (2021)

## Abstract (arXiv)

We utilize neural network embeddings to detect data drift by formulating the drift detection within an appropriate sequential decision framework. This enables control of the false alarm rate although the statistical tests are repeatedly applied. Since change detection algorithms naturally face a tradeoff between avoiding false alarms and quick correct detection, we introduce a loss function which evaluates an algorithm's ability to balance these two concerns, and we use it in a series of experiments.