Universal Domain Adaptation

25 papers with code • 4 benchmarks • 5 datasets

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Use these libraries to find Universal Domain Adaptation models and implementations
3 papers

Most implemented papers

Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation

tim-learn/SHOT ICML 2020

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain.

Upcycling Models under Domain and Category Shift

ispc-lab/glc CVPR 2023

We examine the superiority of our GLC on multiple benchmarks with different category shift scenarios, including partial-set, open-set, and open-partial-set DA.

OVANet: One-vs-All Network for Universal Domain Adaptation

VisionLearningGroup/OVANet ICCV 2021

In this paper, we propose a method to learn the threshold using source samples and to adapt it to the target domain.

LEAD: Learning Decomposition for Source-free Universal Domain Adaptation

ispc-lab/lead CVPR 2024

Besides, LEAD is also appealing in that it is complementary to most existing methods.

GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning

ispc-lab/glc-plus 21 Mar 2024

GLC++ enhances the novel category clustering accuracy of GLC by 4. 3% in open-set scenarios on Office-Home.

Universal Domain Adaptation through Self Supervision

VisionLearningGroup/DANCE NeurIPS 2020

While some methods address target settings with either partial or open-set categories, they assume that the particular setting is known a priori.

Divergence Optimization for Noisy Universal Domain Adaptation

yu1ut/divergence-optimization CVPR 2021

Hence, we consider a new realistic setting called Noisy UniDA, in which classifiers are trained with noisy labeled data from the source domain and unlabeled data with an unknown class distribution from the target domain.

On Universal Black-Box Domain Adaptation

Gorilla-Lab-SCUT/UB2DA 10 Apr 2021

The great promise that UB$^2$DA makes, however, brings significant learning challenges, since domain adaptation can only rely on the predictions of unlabeled target data in a partially overlapped label space, by accessing the interface of source model.

Domain Consensus Clustering for Universal Domain Adaptation

Solacex/Domain-Consensus-Clustering CVPR 2021

To better exploit the intrinsic structure of the target domain, we propose Domain Consensus Clustering (DCC), which exploits the domain consensus knowledge to discover discriminative clusters on both common samples and private ones.

Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation

silvia1993/HyMOS 5 Jul 2021

Vision systems trained in closed-world scenarios fail when presented with new environmental conditions, new data distributions, and novel classes at deployment time.