# CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments

arXiv: https://arxiv.org/abs/2603.17548 · HTML: https://mlsanity.com/paper/2603.17548/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 51 references that are in the map.
- First posted on arXiv 18 Mar 2026.

## Builds on (most cited first, 30 of 30)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Class-Incremental Learning: A Survey](https://mlsanity.com/paper/2302.03648/) (2023)
- [Continual Learning and Catastrophic Forgetting](https://mlsanity.com/paper/2403.05175/) (2024)
- [Recent Advances of Continual Learning in Computer Vision: An Overview](https://mlsanity.com/paper/2109.11369/) (2021)
- [An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions](https://mlsanity.com/paper/2204.08817/) (2022)
- [Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold](https://mlsanity.com/paper/2209.09211/) (2022)
- [Continual Learning for Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/2004.07941/) (2020)
- [NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems](https://mlsanity.com/paper/2011.09144/) (2020)
- [Continual Normalization: Rethinking Batch Normalization for Online Continual Learning](https://mlsanity.com/paper/2203.16102/) (2022)
- [Detection and Continual Learning of Novel Face Presentation Attacks](https://mlsanity.com/paper/2108.12081/) (2021)
- [CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions](https://mlsanity.com/paper/2009.09929/) (2020)
- [An Investigation of Replay-based Approaches for Continual Learning](https://mlsanity.com/paper/2108.06758/) (2021)
- [Mondrian Forests: Efficient Online Random Forests](https://mlsanity.com/paper/1406.2673/) (2014)
- [Continual Learning with Transformers for Image Classification](https://mlsanity.com/paper/2206.14085/) (2022)
- [Online Gradient Boosting](https://mlsanity.com/paper/1506.04820/) (2015)
- [In Defense of the Learning Without Forgetting for Task Incremental Learning](https://mlsanity.com/paper/2107.12304/) (2021)
- [DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors](https://mlsanity.com/paper/2305.05738/) (2023)
- [Calibration of Continual Learning Models](https://mlsanity.com/paper/2404.07817/) (2024)
- [RTRA: Rapid Training of Regularization-based Approaches in Continual Learning](https://mlsanity.com/paper/2312.09361/) (2023)
- [Continual Learning with Strategic Selection and Forgetting for Network Intrusion Detection](https://mlsanity.com/paper/2412.16264/) (2024)
- [Evaluation of Regularization-based Continual Learning Approaches: Application to HAR](https://mlsanity.com/paper/2304.13327/) (2023)
- [Encoding categorical data: Is there yet anything 'hotter' than one-hot encoding?](https://mlsanity.com/paper/2312.16930/) (2023)
- [SOUL: A Semi-supervised Open-world continUal Learning method for Network Intrusion Detection](https://mlsanity.com/paper/2412.00911/) (2024)
- [Decision Boundary-aware Knowledge Consolidation Generates Better Instance-Incremental Learner](https://mlsanity.com/paper/2406.03065/) (2024)

## Abstract (arXiv)

Artificial intelligence systems predominantly rely on static data distributions, making them ineffective in dynamic real-world environments, such as cybersecurity, autonomous transportation, or finance, where data shifts frequently. Continual learning offers a potential solution by enabling models to learn from sequential data while retaining prior knowledge. However, a critical and underexplored issue in this domain is data normalization. Conventional normalization methods, such as min-max scaling, presuppose access to the entire dataset, which is incongruent with the sequential nature of continual learning. In this paper we introduce Continual Learning Adaptive Normalization (CLeAN), a novel adaptive normalization technique designed for continual learning in tabular data. CLeAN involves the estimation of global feature scales using learnable parameters that are updated via an Exponential Moving Average (EMA) module, enabling the model to adapt to evolving data distributions. Through comprehensive evaluations on two datasets and various continual learning strategies, including Resevoir Experience Replay, A-GEM, and EwC we demonstrate that CLeAN not only improves model performance on new data but also mitigates catastrophic forgetting. The findings underscore the importance of adaptive normalization in enhancing the stability and effectiveness of tabular data, offering a novel perspective on the use of normalization to preserve knowledge in dynamic learning environments.