The field of continual learning is rapidly advancing, with a focus on developing methods that can learn continuously without forgetting previously learned tasks. Recent research has explored various approaches to address the challenges of catastrophic forgetting and loss of plasticity in neural networks. One of the key directions is the development of novel architectures and algorithms that can adapt to new tasks while retaining previous knowledge.
Notable papers in this area include the introduction of the Subset Extended Kalman Filter (SEKF) for online model maintenance, the view-batch model for optimizing the recall interval between retraining the same samples, and the Drift-Resistant Space (DRS) for handling feature drifts without requiring explicit feature modeling.
Additionally, researchers have proposed new methods such as Continual Learning with Sampled Quasi-Newton (CSQN) and the Kolmogorov-Arnold Classifier (KAC) to improve the stability and performance of continual learning models. The use of experience replay and Transformers has also been shown to address the loss of plasticity in continual learning.
Other interesting approaches include the incorporation of neurogenesis and neuroplasticity into artificial neural networks, the development of stochastic engrams for efficient continual learning with binarized neural networks, and the introduction of meta-representational predictive coding for biomimetic self-supervised learning.
Some papers that are particularly noteworthy include Staying Alive: Online Neural Network Maintenance and Systemic Drift, which presents the SEKF method for online model maintenance. Do Your Best and Get Enough Rest for Continual Learning, which introduces the view-batch model for optimizing the recall interval. Global Convergence of Continual Learning on Non-IID Data, which provides a general and comprehensive theoretical analysis for continual learning of regression models. LoRA Subtraction for Drift-Resistant Space in Exemplar-Free Continual Learning, which proposes the DRS method for handling feature drifts. Continual Learning With Quasi-Newton Methods, which introduces the CSQN method for improving the stability and performance of continual learning models. Experience Replay Addresses Loss of Plasticity in Continual Learning, which shows that experience replay and Transformers can address the loss of plasticity in continual learning. KAC: Kolmogorov-Arnold Classifier for Continual Learning, which introduces the KAC method for improving the stability and performance of continual learning models. Neuroplasticity in Artificial Intelligence -- An Overview and Inspirations on Drop In & Out Learning, which explores how neurogenesis and neuroplasticity can inspire future AI advances. Stochastic Engrams for Efficient Continual Learning with Binarized Neural Networks, which proposes a novel approach that integrates stochastically-activated engrams for efficient continual learning with binarized neural networks. Architecture of Information, which explores an approach to constructing energy landscapes of a formal neuron and multilayer artificial neural networks. Meta-Representational Predictive Coding: Biomimetic Self-Supervised Learning, which presents a scheme for self-supervised learning within a neurobiologically plausible framework. A Proposal for Networks Capable of Continual Learning, which proposes Modelleyen, an alternative approach with inherent response preservation for continual learning.
Some of these papers, such as Staying Alive: Online Neural Network Maintenance and Systemic Drift and Do Your Best and Get Enough Rest for Continual Learning, are particularly noteworthy for their innovative approaches to continual learning. They demonstrate significant improvements in performance and efficiency, and their methods have the potential to be widely adopted in the field.
