arXiv：cs.LG· Zeki Doruk Erden·· 7 小时前AI 评分33
多尺度结构特征助力发育学习框架实现持续且可理解的视觉识别
Multi-Scale Structural Features for Continual, Comprehensible Visual Recognition in a Developmental Learning Framework
AI 导读
研究者提出一种新的视觉特征表示，在多尺度上编码形状结构，结合边缘与轮廓特征及其空间关系，并集成到无梯度发育学习框架中，在类增量 MNIST 基准上大幅提升识别精度。该方法在同等存储下匹配或超越基于回放与正则化的基线，且不存储任何历史数据，已学类别在新类别引入后仍被保留，学习到的表示保持人类可解释。
正文
Abstract: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 introduce a new visual feature representation that encodes shape structure across multiple scales, capturing edge and contour features together with their spatial relations, and integrate it with the network-refinement learning process; we further improve the learning dynamics and the read-out used to predict from the learned model. The study targets two-dimensional shape, with class-incremental MNIST as a controlled, interpretable benchmark in which continual-learning behavior can be measured directly. Our approach substantially increases accuracy over the prior representation, matching or exceeding replay- and regularisation-based baselines at comparable storage while storing no past data, and preserves the framework's defining behavior: earlier-learned classes are retained as new ones are introduced, with no destructive adaptation, and the learned representations remain human-interpretable. What separates the methods is retention: the baselines surrender most of a just-trained class within its own cycle and relearn it afterwards, which ours does not. The significance lies in the manner of learning. The system integrates information one sample at a time while provably preserving its responses to...
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2607.25531 [cs.LG] |
| (or arXiv:2607.25531v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.25531 arXiv-issued DOI via DataCite |
Submission history
From: Zeki Doruk Erden [view email]
[v1]
Tue, 28 Jul 2026 10:17:54 UTC (660 KB)
[v2]
Mon, 5 Oct 2026 19:20:39 UTC (911 KB)
来源：arXiv：cs.LG · arxiv.org