AI RESEARCH
CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments
arXiv CS.LG
•
ArXi:2603.17548v1 Announce Type: new 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.