Multi-Scale Structural Features for Continual, Comprehensible Visual Recognition in a Developmental Learning Framework
Contemporary machine learning struggles to learn continually, reuse prior knowledge, and expose a comprehensible internal structure. A recently proposed developmental, gradient-free learning framework addresses these limitations by learning a discrete, topological model of its inputs through local variation and selection, yielding an inherent continual-learning guarantee: new observations refine existing structure without overwriting past knowledge, and without replay buffers or predefined task boundaries. Its extension to visual inputs demonstrated this principle on shape recognition, but relied on a feature representation of limited expressivity that capped recognition accuracy. We intr...
| Authors | Zeki Doruk Erden |
|---|---|
| Primary category | cs.LG |
| Published | 28 July 2026 |
| Last updated at source | 5 October 2026 |
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