Semi-supervised Domain Adaptation
49 papers with code • 1 benchmarks • 1 datasets
Libraries
Use these libraries to find Semi-supervised Domain Adaptation models and implementations
VisDA-2017
Most implemented papers
Semi-supervised Domain Adaptation for Dependency Parsing
SUDA-LA/ACL2019-dp-cross-domain
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ACL 2019
During the past decades, due to the lack of sufficient labeled data, most studies on cross-domain parsing focus on unsupervised domain adaptation, assuming there is no target-domain training data.
Open Set Domain Adaptation for Image and Action Recognition
Since this assumption is violated under real-world conditions, we propose an approach for open set domain adaptation where the target domain contains instances of categories that are not present in the source domain.
A Simple Baseline to Semi-Supervised Domain Adaptation for Machine Translation
jind11/DAMT
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State-of-the-art neural machine translation (NMT) systems are data-hungry and perform poorly on new domains with no supervised data.
Contradictory Structure Learning for Semi-supervised Domain Adaptation
canqin001/UODA
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Current adversarial adaptation methods attempt to align the cross-domain features, whereas two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the source domain.
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping
In this paper we present Sourcerer, a Bayesian-inspired, deep learning-based, semi-supervised DA technique for producing land cover maps from SITS data.
Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation
To integrate the strengths of the two classifiers, we apply the well-established co-training framework, in which the two classifiers exchange their high confident predictions to iteratively "teach each other" so that both classifiers can excel in the target domain.
Hard Class Rectification for Domain Adaptation
Second, we further consider that the predictions of target samples belonging to the hard class are vulnerable to perturbations.
Alleviating Human-level Shift : A Robust Domain Adaptation Method for Multi-person Pose Estimation
Sophie-Xu/Pose_DomainAdaption
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Therefore, we propose a novel domain adaptation method for multi-person pose estimation to conduct the human-level topological structure alignment and fine-grained feature alignment.
Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation
In this way, the dual teacher models would transfer acquired inter- and intra-domain knowledge to the student model for further integration and exploitation.
Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency
With our Pretraining and Consistency (PAC) approach, we achieve state of the art target accuracy on this semi-supervised domain adaptation task, surpassing multiple adversarial domain alignment methods, across multiple datasets.
