Neural Cellular Automata Learn General Features in their Hidden Channels

2026-09-18

AI Summary

研究通过引入一种新的迁移学习机制，利用神经元细胞自动机（NCA）的隐藏通道来解决少量样本下的过拟合问题，实现了更优的泛化性能。

Abstract

Modern deep learning models achieve impressive generalization through over-parameterization, but this paradigm often struggles with overfitting and memorization in few-shot regimes. Neural Cellular Automata (NCAs) offer a highly parameter-efficient alternative, yet research has focused primarily on their output, leaving the role of their internal hidden channels largely unexplored. In this paper, we investigate the internal dynamics of NCA hidden channels and introduce a novel transfer-learning mechanism that injects a pretrained teacher's hidden states into a student model to guide early optimization. Evaluated on few-shot and scale-variant MNIST benchmarks, NCAs outperform comparable recurrent and feed-forward architectures, demonstrating superior generalization with a minimal parameter budget (~9,800 parameters). Mechanistic analysis reveals that the hidden channels decouple feature extraction from uniform classification consensus by absorbing morphological complexity and converging to mutually orthogonal states. Furthermore, we demonstrate that these hidden channels capture general, scale-invariant topological primitives rather than class-specific templates. This allows a student model to achieve strong few-shot performance on unseen classes using features transferred from a teacher trained only on a subset of digits (0-5). Our results highlight the potential of utilizing hidden-state dynamics as a robust, decentralized computational substrate for parameter-efficient transfer learning

Problem

• Neural Cellular Automata
• overfitting
• parameter-efficient
• hidden channels
• few-shot

Innovation

• Neural Cellular Automata
• transfer learning
• hidden channels
• few-shot learning
• scale-invariant features