Robustness and Regularization in Hierarchical Re-Basin
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916026576273408 |
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| author | Franke, Benedikt Heinrich, Florian Lange, Markus Raulf, Arne |
| author_facet | Franke, Benedikt Heinrich, Florian Lange, Markus Raulf, Arne |
| contents | This paper takes a closer look at Git Re-Basin, an interesting new approach to merge trained models. We propose a hierarchical model merging scheme that significantly outperforms the standard MergeMany algorithm. With our new algorithm, we find that Re-Basin induces adversarial and perturbation robustness into the merged models, with the effect becoming stronger the more models participate in the hierarchical merging scheme. However, in our experiments Re-Basin induces a much bigger performance drop than reported by the original authors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09174 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robustness and Regularization in Hierarchical Re-Basin Franke, Benedikt Heinrich, Florian Lange, Markus Raulf, Arne Machine Learning This paper takes a closer look at Git Re-Basin, an interesting new approach to merge trained models. We propose a hierarchical model merging scheme that significantly outperforms the standard MergeMany algorithm. With our new algorithm, we find that Re-Basin induces adversarial and perturbation robustness into the merged models, with the effect becoming stronger the more models participate in the hierarchical merging scheme. However, in our experiments Re-Basin induces a much bigger performance drop than reported by the original authors. |
| title | Robustness and Regularization in Hierarchical Re-Basin |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.09174 |