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- Analysis of Representations for Domain Adaptation - IEEE Xplore
Intuitively, a good feature representation is a crucial factor in the success of domain adaptation We formalize this intuition theoretically with a generalization bound for domain adaption
- Unsupervised Domain Adaptation With Hierarchical Masked Dual . . .
As such, a hierarchical masked dual-adversarial DA network (HMDA-DANet) is proposed for cross-domain end-to-end classification of MSRS data
- Reserve to Adapt: Mining Inter-Class Relations for Open-Set Domain . . .
Abstract: Open-Set Domain Adaptation (OSDA) aims at adapting a model trained on a labelled source domain, to an unlabeled target domain that is corrupted with unknown classes
- Bridging Domain Gap of Point Cloud Representations via Self-Supervised . . .
To address such a problem, we introduce a novel scheme for induced geometric invariance of point cloud representations across domains, via regularizing representation learning with two self-supervised geometric augmentation tasks
- Heterogeneous Domain Adaptation via Correlative and Discriminative . . .
To address these issues, in this paper, we put forward a novel heterogeneous domain adaptation method to learn category-correlative and discriminative representations, referred to as correlative and discriminative feature learning (CDFL)
- Multi-Scale Part-Based Feature Representation for 3D Domain . . .
Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence ( Volume: 47 , Issue: 3 , March 2025 ) Article #: Page (s): 1414 - 1430 Date of Publication: 11 November 2024
- Task Nuisance Filtration for Unsupervised Domain Adaptation
In this paper, we address the domain difference as a nuisance, and enables better adaptability of the domains, by encouraging minimality of the target domain representation, disentanglement of the features, and a smoother feature space that cluster better the target data
- Cross-Modality Domain Adaptation Based on Semantic Graph Learning: From . . .
In this article, we employ the domain adaptation (DA) that leverages labeled optical images to better understand unlabeled SAR images
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