SpletA collection of domain generalization papers organized by amber0309. A collection of domain generalization papers organized by jindongwang. A collection of papers on … Splet18. sep. 2024 · This universal framework does not require prior knowledge of the domain of interest. Extensive experiments are conducted on several domain generalization datasets, namely, PACS, Office-Home, VLCS, and Digits. We show that our framework outperforms state-of-the-art domain generalization methods by a large margin. Submission history

SWAD: Domain Generalization by Seeking Flat Minima

SpletDomain generalization (DG) aims to address domain shift simulated by training and evaluating on different domains. DG tasks assume that both task labels and domain … Splet@inproceedings{NEURIPS2024_bcb41ccd, author = {Cha, Junbum and Chun, Sanghyuk and Lee, Kyungjae and Cho, Han-Cheol and Park, Seunghyun and Lee, Yunsung and Park, Sungrae}, booktitle = {Advances in Neural Information Processing Systems}, editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan}, … quarks vulkane

SWAD: Domain Generalization by Seeking Flat Minima - arXiv

Splet08. jun. 2024 · To achieve model generalizability, learning domain-invariant representations Arjovsky et al. (); Ganin et al. for DG has been extensively explored as they are theoretically grounded. However, their performance has been challenged on large-scale DG benchmarks Gulrajani and Lopez-Paz ().On the one hand, strong evidence has revealed the … Splet08. mar. 2013 · The official codes of our CVPR2024 paper: Sharpness-Aware Gradient Matching for Domain Generalization In this paper, we present present an algorithm named Sharpness-Aware Gradient Matching (SAGM) to improve model generalization capability. SpletSWAD shows state-of-the-art performances on five DG benchmarks, namely PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet, with consistent and large margins of +1.6% … hautajaisiin värssy