CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments

• URL: http://arxiv.org/abs/2603.17548v1
• Date: Wed, 18 Mar 2026 09:52:31 GMT
• Title: CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments
• Abstract summary: We introduce Continual Learning Adaptive Normalization (CLeAN), a novel adaptive normalization technique for continuous learning.<n> CLeAN involves the estimation of global feature scales using learnable parameters that are updated via an Exponential Moving Average (EMA) module.<n>We demonstrate that CLeAN not only improves model performance on new data but also mitigates catastrophic forgetting.
• Score: 3.4182067509295915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Can Large Language Models Keep Up? Benchmarking Online Adaptation to Continual Knowledge Streams [78.9560820155271]
  We evaluate online adaptation over streaming, continually updating knowledge.<n> OAKS comprises two datasets: OAKS-BABI and OAKS-Novel.<n>Both state-of-the-art models and agentic memory systems fail to adapt robustly on OAKS.
  arXiv  Detail & Related papers  (2026-03-08T00:38:39Z)
• Entropy-Guided Token Dropout: Training Autoregressive Language Models with Limited Domain Data [89.96277093034547]
  We introduce EntroDrop, an entropy-guided token dropout method that functions as structured data regularization.<n>We show that EntroDrop consistently outperforms standard regularization baselines and maintains robust performance throughout extended multi-epoch training.
  arXiv  Detail & Related papers  (2025-12-29T12:35:51Z)
• Forget Less, Retain More: A Lightweight Regularizer for Rehearsal-Based Continual Learning [51.07663354001582]
  Deep neural networks suffer from catastrophic forgetting, where performance on previous tasks degrades after training on a new task.<n>We present a novel approach to address this challenge, focusing on the intersection of memory-based methods and regularization approaches.<n>We formulate a regularization strategy, termed Information Maximization (IM) regularizer, for memory-based continual learning methods.
  arXiv  Detail & Related papers  (2025-12-01T15:56:00Z)
• Unbiased Online Curvature Approximation for Regularized Graph Continual Learning [9.70311578832594]
  Graph continual learning (GCL) aims to learn from a continuous sequence of graph-based tasks.<n>Regularization methods are vital for preventing catastrophic forgetting in GCL.<n>We propose a new unbiased online curvature approximation of the full Fisher information matrix (FIM) based on the model's current learning state.
  arXiv  Detail & Related papers  (2025-09-16T06:35:13Z)
• Heterogeneous Self-Supervised Acoustic Pre-Training with Local Constraints [64.15709757611369]
  We propose a new self-supervised pre-training approach to dealing with heterogeneous data.<n>The proposed approach can significantly improve the adaptivity of the self-supervised pre-trained model for the downstream supervised fine-tuning tasks.
  arXiv  Detail & Related papers  (2025-08-27T15:48:50Z)
• FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors [50.131271229165165]
  Federated Learning (FL) has emerged as a promising framework for distributed machine learning.<n>Data heterogeneity resulting from differences across user behaviors, preferences, and device characteristics poses a significant challenge for federated learning.<n>We propose Adaptive Weight Aggregation (FedAWA), a novel method that adaptively adjusts aggregation weights based on client vectors during the learning process.
  arXiv  Detail & Related papers  (2025-03-20T04:49:40Z)
• Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shift [2.298932494750101]
  We show that inconsistencies in client-side training distributions substantially degrade the performance of federated learning models.<n>We propose a novel FL framework using a combination of pruning and regularisation of clients' training to improve the sparsity, redundancy, and robustness of neural connections.
  arXiv  Detail & Related papers  (2024-12-19T16:22:37Z)
• Adaptive debiased SGD in high-dimensional GLMs with streaming data [4.704144189806667]
  This paper introduces a novel approach to online inference in high-dimensional generalized linear models.<n>Our method operates in a single-pass mode, making it different from existing methods that require full dataset access or large-dimensional summary statistics storage.<n>The core of our methodological innovation lies in an adaptive descent algorithm tailored for dynamic objective functions, coupled with a novel online debiasing procedure.
  arXiv  Detail & Related papers  (2024-05-28T15:36:48Z)
• A Conditioned Unsupervised Regression Framework Attuned to the Dynamic Nature of Data Streams [0.0]
  This paper presents an optimal strategy for streaming contexts with limited labeled data, introducing an adaptive technique for unsupervised regression. The proposed method leverages a sparse set of initial labels and introduces an innovative drift detection mechanism. To enhance adaptability, we integrate the ADWIN (ADaptive WINdowing) algorithm with error generalization based on Root Mean Square Error (RMSE)
  arXiv  Detail & Related papers  (2023-12-12T19:23:54Z)
• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective [52.497514255040514]
  This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes. Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2021-05-03T07:23:39Z)
• Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment [55.91291559442884]
  This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. We propose to build the notion of continual learning into the modeling process of learning wireless systems. Our design is based on a novel min-max formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2020-11-16T08:24:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.