AI 中文总结
研究针对持续、可理解视觉识别问题，提出跨多尺度编码形状结构的新视觉特征表示并集成到网络细化学习中，改进学习动态和读出，在类增量MNIST上大幅提准确率，保留早期学习类且表示可解释。
AI 中文摘要
当代机器学习在持续学习、重用先验知识和揭示可理解内部结构方面存在困难。最近提出的发展性、无梯度学习框架通过局部变化和选择学习输入的离散拓扑模型来解决这些限制，在形状识别上展示了该原理，但依赖的特征表示表现力有限。我们引入跨多尺度编码形状结构的新视觉特征表示并与网络细化学习过程集成，还改进了学习动态和读出。以类增量MNIST为基准测试，我们的方法大幅提高准确率，保留早期学习类，学习表示可解释，且学习方式有意义。
英文摘要
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...
