Continual learning (CL) algorithms strive to acquire new knowledge while
preserving prior information. However, this stability-plasticity trade-off
remains a central challenge. This paper introduces a framework that dissects
this trade-off, offering valuable insights into CL algorithms. The
Readout-Decomposition of Activation Change (RDAC) framework first addresses the
stability-plasticity dilemma and its relation to catastrophic forgetting. It
relates learning-induced activation changes in the range of prior readouts to
the degree of stability and changes in the null space to the degree of
plasticity. In deep non-linear networks tackling split-CIFAR-110 tasks, the
framework clarifies the stability-plasticity trade-offs of the popular
regularization algorithms Synaptic intelligence (SI), Elastic-weight
consolidation (EWC), and learning without Forgetting (LwF), and replay-based
algorithms Gradient episodic memory (GEM), and data replay. GEM and data replay
preserved stability and plasticity, while SI, EWC, and LwF traded off
plasticity for stability. The inability of the regularization algorithms to
maintain plasticity was linked to them restricting the change of activations in
the null space of the prior readout. Additionally, for one-hidden-layer linear
neural networks, we derived a gradient decomposition algorithm to restrict
activation change only in the range of the prior readouts, to maintain high
stability while not further sacrificing plasticity. Results demonstrate that
the algorithm maintained stability without significant plasticity loss. The
RDAC framework informs the behavior of existing CL algorithms and paves the way
for novel CL approaches. Finally, it sheds light on the connection between
learning-induced activation/representation changes and the stability-plasticity
dilemma, also offering insights into representational drift in biological
systems.

连续学习算法中的稳定性和可塑性之间的权衡问题是本研究的核心挑战，本文引入了 RDAC 框架来解决这一问题，并研究了常用的正则化算法在稳定性和可塑性之间的权衡情况，在深层非线性网络和单隐藏层线性神经网络上进行了验证实验，结果表明该算法在保持稳定性的同时并没有明显损失可塑性，为现有的连续学习算法提供了有价值的见解，并为新的连续学习方法铺平了道路，在生物系统中对学习诱导的激活 / 表示变化与稳定性 - 可塑性困境的关系以及再现漂移问题也提供了新的视角。

在持续学习中平衡稳定性和可塑性：激活变化的输出分解（RDAC）框架

Balancing stability and plasticity in continual learning: the  readout-decomposition of activation change (RDAC) framework

To cope with real-world dynamics, an intelligent agent needs to incrementally
acquire, update, accumulate, and exploit knowledge throughout its lifetime.
This ability, known as continual learning, provides a foundation for AI systems
to develop themselves adaptively. In a general sense, continual learning is
explicitly limited by catastrophic forgetting, where learning a new task
usually results in a dramatic performance drop of the old tasks. Beyond this,
increasingly numerous advances have emerged in recent years that largely extend
the understanding and application of continual learning. The growing and
widespread interest in this direction demonstrates its realistic significance
as well as complexity. In this work, we present a comprehensive survey of
continual learning, seeking to bridge the basic settings, theoretical
foundations, representative methods, and practical applications. Based on
existing theoretical and empirical results, we summarize the general objectives
of continual learning as ensuring a proper stability-plasticity trade-off and
an adequate intra/inter-task generalizability in the context of resource
efficiency. Then we provide a state-of-the-art and elaborated taxonomy,
extensively analyzing how representative strategies address continual learning,
and how they are adapted to particular challenges in various applications.
Through an in-depth discussion of continual learning in terms of the current
trends, cross-directional prospects and interdisciplinary connections with
neuroscience, we believe that such a holistic perspective can greatly
facilitate subsequent exploration in this field and beyond.

本研究全面综述了不断学习的基本设置，理论基础，代表性方法和实际应用，总结了不断学习的目标，并通过当前趋势，跨方向前景和跨学科与神经科学的联系开展了深入讨论。