Model Fusion of Heterogeneous Neural Networks via Cross-Layer Alignment

• URL: http://arxiv.org/abs/2110.15538v1
• Date: Fri, 29 Oct 2021 05:02:23 GMT
• Title: Model Fusion of Heterogeneous Neural Networks via Cross-Layer Alignment
• Authors: Dang Nguyen and Khai Nguyen and Dinh Phung and Hung Bui and Nhat Ho
• Abstract summary: We propose a novel model fusion framework, named CLAFusion, to fuse neural networks with a different number of layers. Based on the cross-layer alignment, our framework balances the number of layers of neural networks before applying layer-wise model fusion.
• Score: 17.735593218773758
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Layer-wise model fusion via optimal transport, named OTFusion, applies soft neuron association for unifying different pre-trained networks to save computational resources. While enjoying its success, OTFusion requires the input networks to have the same number of layers. To address this issue, we propose a novel model fusion framework, named CLAFusion, to fuse neural networks with a different number of layers, which we refer to as heterogeneous neural networks, via cross-layer alignment. The cross-layer alignment problem, which is an unbalanced assignment problem, can be solved efficiently using dynamic programming. Based on the cross-layer alignment, our framework balances the number of layers of neural networks before applying layer-wise model fusion. Our synthetic experiments indicate that the fused network from CLAFusion achieves a more favorable performance compared to the individual networks trained on heterogeneous data without the need for any retraining. With an extra fine-tuning process, it improves the accuracy of residual networks on the CIFAR10 dataset. Finally, we explore its application for model compression and knowledge distillation when applying to the teacher-student setting.

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