Szczegóły publikacji
Opis bibliograficzny
SatSOM: saturation Self-Organizing Maps for continual learning / Igor Urbanik, Paweł GAJEWSKI // Journal of Artificial Intelligence and Soft Computing Research [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2449-6499 . — 2026 — vol. 16 no. 3, s. 293–310. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 309–310, Abstr. — Publikacja dostępna online od: 2026-02-25
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 166595 |
|---|---|
| Data dodania do BaDAP | 2026-03-19 |
| Tekst źródłowy | URL |
| DOI | 10.2478/jaiscr-2026-0015 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Journal of Artificial Intelligence and Soft Computing Research |
Abstract
Continual learning poses a fundamental challenge for neural systems, which typically suffer from catastrophic forgetting when exposed to sequential tasks. Self-Organizing Maps (SOMs), despite their inherent interpretability and efficiency, also exhibit this vulnerability. In this paper, we introduce Saturation Self-Organizing Maps (SatSOM)—an extension designed to enhance knowledge retention in continual learning scenarios. Sat-SOM incorporates a novel saturation mechanism that progressively reduces the learning rate and neighborhood radius of neurons as they accumulate information. This dynamic effectively stabilizes well-trained neurons, redirecting new learning to underutilized regions of the map. To further accommodate tasks of unknown complexity, we introduce a dynamic variant capable of adaptive grid expansion. We evaluate SatSOM on sequential versions of the FashionMNIST and KMNIST datasets, showing that it significantly outperforms existing SOM-based methods and approaches the retention capabilities of a k-nearest neighbors (kNN) baseline. Ablation studies confirm the critical role of the saturation mechanism. SatSOM offers a lightweight and interpretable solution for sequential learning and provides a foundation for implementing adaptive plasticity in complex architectures.
