Model Fusion via Retrofitting
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913170219597824 |
|---|---|
| author | Luenam, Phoomraphee Spanopoulos, Andreas Sant, Amit Hofmann, Thomas Anagnostidis, Sotiris Singh, Sidak Pal |
| author_facet | Luenam, Phoomraphee Spanopoulos, Andreas Sant, Amit Hofmann, Thomas Anagnostidis, Sotiris Singh, Sidak Pal |
| contents | Model fusion seeks to combine independently trained neural networks into a single model without retraining, but is complicated by representational divergence arising from permutation invariance, random initialization, and heterogeneous training data. Existing methods struggle particularly in zero-shot settings under non-IID data distributions, and are often limited to specific architectures or pairwise fusion. We introduce a neuron-centric family of fusion algorithms that frames fusion as a principled representation-matching problem: intermediate neurons across parent models are grouped into target representations, which the fused model's corresponding sub-networks are then trained to approximate. Unlike prior work, our approach incorporates neuron attribution scores to bias alignment toward salient features, and can be applied to any architecture modularizable as a DAG of levels -- empirically validated on VGGs, ResNets, and ViTs. Experiments across standard benchmarks show consistent improvements over existing fusion methods, with the largest gains in zero-shot and non-IID scenarios. Code is available at https://github.com/AndrewSpano/model-fusion-via-retrofitting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00037 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model Fusion via Retrofitting Luenam, Phoomraphee Spanopoulos, Andreas Sant, Amit Hofmann, Thomas Anagnostidis, Sotiris Singh, Sidak Pal Machine Learning Artificial Intelligence I.2.6; I.2.1 Model fusion seeks to combine independently trained neural networks into a single model without retraining, but is complicated by representational divergence arising from permutation invariance, random initialization, and heterogeneous training data. Existing methods struggle particularly in zero-shot settings under non-IID data distributions, and are often limited to specific architectures or pairwise fusion. We introduce a neuron-centric family of fusion algorithms that frames fusion as a principled representation-matching problem: intermediate neurons across parent models are grouped into target representations, which the fused model's corresponding sub-networks are then trained to approximate. Unlike prior work, our approach incorporates neuron attribution scores to bias alignment toward salient features, and can be applied to any architecture modularizable as a DAG of levels -- empirically validated on VGGs, ResNets, and ViTs. Experiments across standard benchmarks show consistent improvements over existing fusion methods, with the largest gains in zero-shot and non-IID scenarios. Code is available at https://github.com/AndrewSpano/model-fusion-via-retrofitting. |
| title | Model Fusion via Retrofitting |
| topic | Machine Learning Artificial Intelligence I.2.6; I.2.1 |
| url | https://arxiv.org/abs/2507.00037 |