Universal Domain Adaptation
25 papers with code • 4 benchmarks • 5 datasets
Libraries
Use these libraries to find Universal Domain Adaptation models and implementations
Office-Home
DomainNet
Office-31
VisDA-2017
All-day CityScapes
Most implemented papers
VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data
VisionLearningGroup/visda21-dev
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Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i. e., the same domain.
OneRing: A Simple Method for Source-free Open-partial Domain Adaptation
In this paper, we investigate Source-free Open-partial Domain Adaptation (SF-OPDA), which addresses the situation where there exist both domain and category shifts between source and target domains.
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks
We apply this parameterization to the state-of-art domain adaptation methods and show that it has almost the same expressiveness as the full parameter space.
Unified Optimal Transport Framework for Universal Domain Adaptation
Notably, UniOT is the first method with the capability to automatically discover and recognize private categories in the target domain for UniDA.
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS).
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All in One Classifier
zangzelin/code_san_share
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ICCV 2023
The SCL loss weakens the adverse effects of the data augmentation view-noise problem which is amplified in domain transfer tasks.
Universal Domain Adaptation for Remote Sensing Image Scene Classification
zhu-xlab/unida
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Empirical results show that the proposed model is effective and practical for remote sensing image scene classification, regardless of whether the source data is available or not.
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
This algorithm performs semi-supervised domain adaptation and can be applied to datasets with different data distributions and class overlaps.
Domain Adaptation for Time Series Under Feature and Label Shifts
Additionally, the label distributions of tasks in the source and target domains can differ significantly, posing difficulties in addressing label shifts and recognizing labels unique to the target domain.
Noisy Universal Domain Adaptation via Divergence Optimization for Visual Recognition
yu1ut/divergence-optimization
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To transfer the knowledge learned from a labeled source domain to an unlabeled target domain, many studies have worked on universal domain adaptation (UniDA), where there is no constraint on the label sets of the source domain and target domain.
