The paper introduces a generative continual learning framework that uses growing self‑organizing maps (GSOMs) enhanced with learned distributional statistics and encoder‑decoder models for class‑incremental learning. GSOM units store mean, variance, and covariance estimates to synthesize replay samples, enabling exemplar‑free training without raw data or explicit task boundaries. Experiments on multiple benchmarks show that the unsupervised method competes with supervised memory‑based approaches and outperforms memory‑free baselines, especially in single‑class incremental scenarios, and provides baseline results for TinyImageNet and MiniImageNet.
By Pujan Thapa, Alexander Ororbia, Travis Desell
arXiv:2603. 11201v3 Announce Type: replace-cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams.
By Haihua Luo, Xuming Ran, Tommi K\"arkk\"ainen, Huiyan Xue, Zhonghua Chen, Qi Xu, Fengyu Cong
arXiv:2608. 15854v1 Announce Type: new Abstract: Catastrophic forgetting remains a fundamental obstacle to continual learning, where neural networks lose previously acquired knowledge while learning new tasks.
By Maksim A. Kazanskii
The paper argues that catastrophic forgetting and loss of plasticity alone cannot explain why naive sequential training underperforms offline joint training. It introduces data co-observation as a third factor, showing that observing training data together consistently improves performance across supervised and self-supervised settings. The study also reinterprets common continual learning methods, suggesting that memory replay’s success stems from restoring co-observation benefits rather than merely mitigating forgetting.
By Timm Hess, Abhishek Jha, Gido M. van de Ven, Tinne Tuytelaars
arXiv:2606. 31275v1 Announce Type: cross Abstract: Online Continual Self-Supervised Learning (OCSSL) aims to learn representations from a continuous stream of unlabeled data, without knowledge of task boundaries and under memory constraints.
By Julien Lefebvre, Stefan Duffner, Mathieu Lefort
arXiv:2603. 12658v2 Announce Type: replace-cross Abstract: Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating catastrophic forgetting, a critical limitation of the static pre-training paradigm inherent to modern LLMs.
By Hongyang Chen, Zhongwu Sun, Hongfei Ye, Kunchi Li, Xuemin Lin