Title: Model Fusion via Neuron Interpolation
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Model Fusion, Neuron Importance, Non-IID Splits, Layer Alignment

Abstract: Model fusion aims to combine the knowledge of multiple models by creating one representative model that captures the strengths of all of its parents. However, this process is non-trivial due to differences in internal representations, which can stem from permutation invariance, random initialization, or differently distributed training data. We present a novel, neuron-centric family of model fusion algorithms designed to integrate multiple trained neural networks into a single network effectively regardless of training data distribution. Our algorithms group intermediate neurons of parent models to create target representations that the fused model approximates with its corresponding sub-network. Unlike prior approaches, our approach incorporates neuron attribution scores into the fusion process. Furthermore, our algorithms can generalize to arbitrary layer types. Experimental results on various benchmark datasets demonstrate that our algorithms consistently outperform previous fusion techniques, particularly in zero-shot and non-IID fusion scenarios.

TL;DR: Fusing models by grouping neurons using L2 distance, representing groups in the fused model, and boosting performance with importance scores