Abstract
As modern deep learning architectures grow in complexity, representational ambiguity emerges as a critical barrier to their interpretability and reliable merging. For ReLU networks, identical functional mappings can be achieved through entirely different weight configurations due to algebraic symmetries: neuron permutation and positive diagonal scaling. Consequently, traditional parameter-based comparison methods exhibit extreme instability to slight weight perturbations during training. This paper proposes a mathematically grounded approach to constructing a stable canonical representation of neural networks and a robust functional similarity metric. We shift focus from comparing raw weights to analyzing the topology of neuron activation regions. The algorithm first eliminates scaling ambiguity via L2-normalization of weight vectors with subsequent layer compensation. Next, discrete approximations of activation regions are generated as binary functional signatures evaluated over a dat