Abstract
Continual learning poses a fundamental challenge for neural systems, which often suffer from catastrophic forgetting when exposed to sequential tasks. Self-Organizing Maps (SOMs), despite their interpretability and efficiency, are not immune to this issue. In this paper, we introduce Saturation Self-Organizing Maps (SatSOM)-an extension of SOMs designed to improve knowledge retention in continual learning scenarios. SatSOM incorporates a novel saturation mechanism that gradually reduces the learning rate and neighborhood radius of neurons as they accumulate information. This effectively freezes well-trained neurons and redirects learning to underutilized areas of the map.
Related Papers
RegCL: Continual Adaptation of Segment Anything Model via Model Merging2025-07-16Information-Theoretic Generalization Bounds of Replay-based Continual Learning2025-07-16PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning2025-07-16Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime2025-07-15A Neural Network Model of Complementary Learning Systems: Pattern Separation and Completion for Continual Learning2025-07-15LifelongPR: Lifelong knowledge fusion for point cloud place recognition based on replay and prompt learning2025-07-14Overcoming catastrophic forgetting in neural networks2025-07-14Continual Reinforcement Learning by Planning with Online World Models2025-07-12