# A Unified and General Framework for Continual Learning

arXiv: https://arxiv.org/abs/2403.13249 · HTML: https://mlsanity.com/paper/2403.13249/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 62 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 30 of 44)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [Dark Experience for General Continual Learning: a Strong, Simple Baseline](https://mlsanity.com/paper/2004.07211/) (2020)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [Gradient based sample selection for online continual learning](https://mlsanity.com/paper/1903.08671/) (2019)
- [Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference](https://mlsanity.com/paper/1810.11910/) (2018)
- [Progress & Compress: A scalable framework for continual learning](https://mlsanity.com/paper/1805.06370/) (2018)
- [What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation](https://mlsanity.com/paper/2008.03703/) (2020)
- [Making AI Forget You: Data Deletion in Machine Learning](https://mlsanity.com/paper/1907.05012/) (2019)
- [Gradient Projection Memory for Continual Learning](https://mlsanity.com/paper/2103.09762/) (2021)
- [Variational Continual Learning](https://mlsanity.com/paper/1710.10628/) (2017)
- [On Tiny Episodic Memories in Continual Learning](https://mlsanity.com/paper/1902.10486/) (2019)
- [Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting](https://mlsanity.com/paper/1904.00310/) (2019)
- [Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition](https://mlsanity.com/paper/1503.02101/) (2015)
- [Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis](https://mlsanity.com/paper/1702.03849/) (2017)
- [New Insights on Reducing Abrupt Representation Change in Online Continual Learning](https://mlsanity.com/paper/2104.05025/) (2021)
- [Compacting, Picking and Growing for Unforgetting Continual Learning](https://mlsanity.com/paper/1910.06562/) (2019)
- [Using Hindsight to Anchor Past Knowledge in Continual Learning](https://mlsanity.com/paper/2002.08165/) (2020)
- [Practical Deep Learning with Bayesian Principles](https://mlsanity.com/paper/1906.02506/) (2019)
- [DeltaGrad: Rapid retraining of machine learning models](https://mlsanity.com/paper/2006.14755/) (2020)
- [A Complete Recipe for Stochastic Gradient MCMC](https://mlsanity.com/paper/1506.04696/) (2015)
- [Machine Unlearning via Algorithmic Stability](https://mlsanity.com/paper/2102.13179/) (2021)

## Built on by (most cited first, 20 of 20)

- [Unforgotten Safety: Preserving Safety Alignment of Large Language Models with Continual Learning](https://mlsanity.com/paper/2512.10150/) (2025)
- [KIF: Knowledge Identification and Fusion for Language Model Continual Learning](https://mlsanity.com/paper/2408.05200/) (2024)
- [Integrating Present and Past in Unsupervised Continual Learning](https://mlsanity.com/paper/2404.19132/) (2024)
- [A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning](https://mlsanity.com/paper/2505.15178/) (2025)
- [Learning without Isolation: Pathway Protection for Continual Learning](https://mlsanity.com/paper/2505.18568/) (2025)
- [Sparse Model Inversion: Efficient Inversion of Vision Transformers for Data-Free Applications](https://mlsanity.com/paper/2510.27186/) (2025)
- [In Praise of Stubbornness: An Empirical Case for Cognitive-Dissonance Aware Continual Update of Knowledge in LLMs](https://mlsanity.com/paper/2502.04390/) (2025)
- [Self-Controlled Dynamic Expansion Model for Continual Learning](https://mlsanity.com/paper/2504.10561/) (2025)
- [Forget Less, Retain More: A Lightweight Regularizer for Rehearsal-Based Continual Learning](https://mlsanity.com/paper/2512.01818/) (2025)
- [Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights](https://mlsanity.com/paper/2604.17578/) (2026)
- [Two-Way Is Better Than One: Bidirectional Alignment with Cycle Consistency for Exemplar-Free Class-Incremental Learning](https://mlsanity.com/paper/2606.05675/) (2026)
- [Theoretical Foundations of Continual Learning via Drift-Plus-Penalty](https://mlsanity.com/paper/2606.08452/) (2026)
- [Balanced Gradient Sample Retrieval for Enhanced Knowledge Retention in Proxy-based Continual Learning](https://mlsanity.com/paper/2412.14430/) (2024)
- [Incrementally Learning Multiple Diverse Data Domains via Multi-Source Dynamic Expansion Model](https://mlsanity.com/paper/2501.08878/) (2025)
- [Learning Dynamic Representations via An Optimally-Weighted Maximum Mean Discrepancy Optimization Framework for Continual Learning](https://mlsanity.com/paper/2501.12121/) (2025)
- [Dynamic Dual Buffer with Divide-and-Conquer Strategy for Online Continual Learning](https://mlsanity.com/paper/2505.18101/) (2025)
- [What Neuroscience Can Teach AI About Learning in Continuously Changing Environments](https://mlsanity.com/paper/2507.02103/) (2025)
- [Compensating Visual Insufficiency with Stratified Language Guidance for Long-Tail Class Incremental Learning](https://mlsanity.com/paper/2603.21708/) (2026)
- [Federated Continual Learning as a Distributed Drift-Plus-Penalty Control Problem](https://mlsanity.com/paper/2608.21539/) (2026)
- [Do More Modalities Always Help? A Geometric Perspective on Missing-Modality Robustness](https://mlsanity.com/paper/2610.04792/) (2026)

## Abstract (arXiv)

Continual Learning (CL) focuses on learning from dynamic and changing data distributions while retaining previously acquired knowledge. Various methods have been developed to address the challenge of catastrophic forgetting, including regularization-based, Bayesian-based, and memory-replay-based techniques. However, these methods lack a unified framework and common terminology for describing their approaches. This research aims to bridge this gap by introducing a comprehensive and overarching framework that encompasses and reconciles these existing methodologies. Notably, this new framework is capable of encompassing established CL approaches as special instances within a unified and general optimization objective. An intriguing finding is that despite their diverse origins, these methods share common mathematical structures. This observation highlights the compatibility of these seemingly distinct techniques, revealing their interconnectedness through a shared underlying optimization objective. Moreover, the proposed general framework introduces an innovative concept called refresh learning, specifically designed to enhance the CL performance. This novel approach draws inspiration from neuroscience, where the human brain often sheds outdated information to improve the retention of crucial knowledge and facilitate the acquisition of new information. In essence, refresh learning operates by initially unlearning current data and subsequently relearning it. It serves as a versatile plug-in that seamlessly integrates with existing CL methods, offering an adaptable and effective enhancement to the learning process. Extensive experiments on CL benchmarks and theoretical analysis demonstrate the effectiveness of the proposed refresh learning. Code is available at urlhttps://github.com/joey-wang123/CL-refresh-learning.