paper-with-me

Papers

Robustness and Regularization in Hierarchical Re-Basin

2025-10-10 · Benedikt Franke, Florian Heinrich, Markus Lange, Arne Raulf arxiv

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.

📄 PDF Abstract BibTeX arXiv:2510.09174

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph

2015-05-01 · Aleksandar Zlateski, H. Sebastian Seung

We present a method for hierarchical image segmentation that defines a disaffinity graph on the image, over-segments it into watershed basins, defines a new graph on the basins, and then merges basins with a modified, si…

ClusteringImage SegmentationSemantic Segmentation

To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning

2023-03-06 · NeurIPS 2023 11 · Ildus Sadrtdinov, Dmitrii Pozdeev, Dmitry Vetrov, Ekaterina Lobacheva

Transfer learning and ensembling are two popular techniques for improving the performance and robustness of neural networks. Due to the high cost of pre-training, ensembles of models fine-tuned from a single pre-trained …

DiversityTransfer Learning

Upper bound for the stability of Boolean networks

2025-06-14 · Venkata Sai Narayana Bavisetty, Matthew Wheeler, Reinhard Laubenbacher, Claus Kadelka

Boolean networks, inspired by gene regulatory networks, were developed to understand the complex behaviors observed in biological systems, with network attractors corresponding to biological phenotypes or cell types. In …

The conjugated null space method of blind PSF estimation and deconvolution optimization

2015-02-26 · Yuriy A. Bunyak, Roman N. Kvetnyy, Olga Yu. Sofina

We have shown that the vector of the point spread function (PSF) lexicographical presentation belongs to the left side conjugated null space (NS) of the autoregression (AR) matrix operator on condition the AR parameters …

Denoisingimage smoothing

Probabilistic Inverse Modeling: An Application in Hydrology

2022-10-12 · Somya Sharma, Rahul Ghosh, Arvind Renganathan, Xiang Li 외

The astounding success of these methods has made it imperative to obtain more explainable and trustworthy estimates from these models. In hydrology, basin characteristics can be noisy or missing, impacting streamflow pre…