Unsupervised Continual Learning with Growing Self-Organizing Maps and Synthetic Replay

2026-08-27

AI Summary

本文提出了一种基于增长自组织映射的无监督持续学习框架，通过分布统计记忆和编码解码模型生成合成样本进行重放，解决了在无需存储原始数据的情况下实现类增量学习的问题。

Abstract

This work presents a generative continual learning framework based on growing self-organizing maps (GSOMs) that are augmented with learned distributional statistics as well as encoder-decoder models for class-incremental learning. The proposed approach enables exemplar-free replay using distributional statistical memory, which eliminates the need to store raw data. Each GSOM unit maintains its own mean, variance, and covariance estimates, which are subsequently used to generate synthetic samples for replay; in encoder-decoder configurations, these samples are then decoded back into the input space (via ancestral sampling) for subsequent training. Our method is fully unsupervised, as it does not rely on explicit task boundaries or class labels during training. Results across multiple benchmarks show that the proposed approach achieves performance competitive even with supervised state-of-the-art memory-based methods while consistently outperforming memory-free approaches. In several settings, our framework matches or exceeds existing baselines, particularly in challenging single-class incremental scenarios. We also provide baseline results for single-class incremental TinyImageNet and MiniImageNet, offering a useful reference for future work. This work highlights the effectiveness of an unsupervised, adaptive, topology-driven neural form of statistical replay as a scalable, flexible approach to continual learning.

Problem

• Unsupervised Continual Learning
• Class-incremental Learning
• Synthetic Replay
• Growing Self-Organizing Maps
• Distributional Statistical Memory

Innovation

• unsupervised continual learning
• growing self-organizing maps (GSOMs)
• synthetic replay
• distributional statistical memory
• encoder-decoder models