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[2507.02310] Holistic Continual Learning under Concept Drift with Adaptive Memory Realignment
Summary
This paper presents a novel framework for continual learning that addresses concept drift through Adaptive Memory Realignment (AMR), enhancing model adaptability while minimizing computational overhead.
Why It Matters
As data streams in real-world applications are often non-static, traditional continual learning methods fall short. This research offers a solution that balances the need for stability in learned tasks with the adaptability required to handle evolving data distributions, making it highly relevant for AI applications in dynamic environments.
Key Takeaways
- AMR provides a lightweight alternative to Full Relearning, reducing data and computational needs.
- The framework effectively counters concept drift while maintaining high accuracy.
- Four new concept drift variants of standard benchmarks are introduced for reproducible evaluation.
- AMR selectively updates memory to align with current data distributions.
- This approach enhances the scalability of continual learning in non-stationary environments.
Computer Science > Machine Learning arXiv:2507.02310 (cs) [Submitted on 3 Jul 2025 (v1), last revised 12 Feb 2026 (this version, v2)] Title:Holistic Continual Learning under Concept Drift with Adaptive Memory Realignment Authors:Alif Ashrafee, Jedrzej Kozal, Michal Wozniak, Bartosz Krawczyk View a PDF of the paper titled Holistic Continual Learning under Concept Drift with Adaptive Memory Realignment, by Alif Ashrafee and 3 other authors View PDF HTML (experimental) Abstract:Traditional continual learning methods prioritize knowledge retention and focus primarily on mitigating catastrophic forgetting, implicitly assuming that the data distribution of previously learned tasks remains static. This overlooks the dynamic nature of real-world data streams, where concept drift permanently alters previously seen data and demands both stability and rapid adaptation. We introduce a holistic framework for continual learning under concept drift that simulates realistic scenarios by evolving task distributions. As a baseline, we consider Full Relearning (FR), in which the model is retrained from scratch on newly labeled samples from the drifted distribution. While effective, this approach incurs substantial annotation and computational overhead. To address these limitations, we propose Adaptive Memory Realignment (AMR), a lightweight alternative that equips rehearsal-based learners with a drift-aware adaptation mechanism. AMR selectively removes outdated samples of drifted classes fro...