SOURCE-LINKED INTELLIGENCE
Neural Cellular Automata Learn General Features in their Hidden Channels
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 opti
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-18T14:58:18.000Z
First collected: 2026-09-23T13:51:27.104Z. This is not the publication date.