CCA Merge: Merging Many Neural Networks with Canonical Correlation Analysis
Keywords: Model merging, Linear mode connectivity
TL;DR: We introduce a novel model fusion method based on Canonical Correlation Analysis, which is able to merge many models into one while achieving lower drops in accuracy than past methods.
Abstract: Combining the predictions of multiple trained models through ensembling is generally a good way to improve accuracy by leveraging the different learned features of the models, however it comes with high computational and storage costs. Model fusion, the act of merging multiple models into one by combining their parameters reduces these costs but doesn't work as well in practice. Indeed, neural network loss landscapes are high-dimensional and non-convex and the minima found through learning are typically separated by high loss barriers. Numerous recent works have been focused on finding permutations matching one network features to the features of a second one, lowering the loss barrier on the linear path between them in parameter space. However, permutations are restrictive since they assume a one-to-one mapping between the different models' neurons exists. We propose a new model merging algorithm, CCA Merge, which is based on Canonical Correlation Analysis and aims to maximize the correlations between linear combinations of the model features. We show that our method of aligning models leads to lower accuracy barriers when averaging model parameters than past methods. We also extend this analysis into the harder many models setting where more than 2 models are merged, and we find that CCA Merge works significantly better in this setting than past methods.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 4463
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