# Open World Autoencoding Drift Detection with Novel Class Recognition in Tabular Non-stationary Data Streams

arXiv: https://arxiv.org/abs/2605.29834 · HTML: https://mlsanity.com/paper/2605.29834/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 6 of 6)

- [Robust Estimators in High Dimensions without the Computational Intractability](https://mlsanity.com/paper/1604.06443/) (2016)
- [C2AE: Class Conditioned Auto-Encoder for Open-set Recognition](https://mlsanity.com/paper/1904.01198/) (2019)
- [A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams](https://mlsanity.com/paper/2106.09170/) (2021)
- [Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation](https://mlsanity.com/paper/2305.08977/) (2023)
- [Synthetic Non-stationary Data Streams for Recognition of the Unknown](https://mlsanity.com/paper/2505.13745/) (2025)
- [Unsupervised Concept Drift Detection based on Parallel Activations of Neural Network](https://mlsanity.com/paper/2404.07776/) (2024)

## Abstract (arXiv)

Data stream processing has become a landmark in modern machine learning applications, with concept drifts and novel class appearances posing the primary challenges faced by sophisticated recognition methods. This work proposes an unsupervised concept drift detection method that identifies shifts in known class distributions based on the reconstruction errors of an autoencoder, while also enabling the recognition of novel class samples through density estimation of a proxy representation of samples. Using mirrored autoencoders allows for independent incremental adaptation to changing problem distributions for the two considered tasks, resulting in continuous adjustment to evolving concepts and reliable recognition of unknown samples. Conducted experiments used a diverse set of synthetic tabular data streams, where both concept drifts and the emergence of novelties were observed. The results show that the proposed approach is competitive with current state-of-the-art unsupervised drift detectors and novelty classifiers.