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arXiv Machine Learning By Pujan Thapa, Alexander Ororbia, Travis Desell

Class Incremental Continual Learning with Self-Organizing Maps and Synthetic Replay

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The paper presents a generative continual learning framework that extends self‑organizing maps (SOMs) with learned distributional statistics and encoder–decoder models for class‑incremental learning. By storing running means, variances, and covariances for each SOM unit, the method can generate synthetic samples for replay without storing raw data, enabling exemplar‑free learning. Experiments on CIFAR‑10, CIFAR‑100, and TinyImageNet show competitive or superior performance compared to state‑of‑the‑art memory‑based and memory‑free methods, and the approach also allows easy visualization and post‑training generative use.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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