Semi-supervised Domain Adaptation
48 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
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
google-research/adamatch
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ICLR 2022
We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one.
Semi-supervised Domain Adaptation via Minimax Entropy
VisionLearningGroup/SSDA_MME
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ICCV 2019
Contemporary domain adaptation methods are very effective at aligning feature distributions of source and target domains without any target supervision.
Reducing Domain Gap by Reducing Style Bias
facebookresearch/DomainBed
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CVPR 2021
Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift.
Cross-modal Learning for Domain Adaptation in 3D Semantic Segmentation
valeoai/xmuda
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Domain adaptation is an important task to enable learning when labels are scarce.
Multi-Source Domain Adaptation and Semi-Supervised Domain Adaptation with Focus on Visual Domain Adaptation Challenge 2019
Panda-Peter/visda2019-multisource
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Semi-Supervised Domain Adaptation: For this task, we adopt a standard self-learning framework to construct a classifier based on the labeled source and target data, and generate the pseudo labels for unlabeled target data.
Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation
tkkim93/ape
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ECCV 2020
Finally, the exploration scheme locally aligns features in a class-wise manner complementary to the attraction scheme by selectively aligning unlabeled target features complementary to the perturbation scheme.
Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation
lijichang/CVPR2021-SSDA
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CVPR 2021
Pseudo labeling expands the number of ``labeled" samples in each class in the target domain, and thus produces a more robust and powerful cluster core for each class to facilitate adversarial learning.
Probabilistic Contrastive Learning for Domain Adaptation
ljjcoder/PCL
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However, it is undesirably observed that the standard contrastive paradigm (features+$\ell_{2}$ normalization) only brings little help for domain adaptation.
Truly Generalizable Radiograph Segmentation with Conditional Domain Adaptation
We merge these unsupervised networks with supervised deep semantic segmentation architectures in order to create a semi-supervised method capable of learning from both unlabeled and labeled data, whenever labeling is available.
Zoho at SemEval-2019 Task 9: Semi-supervised Domain Adaptation using Tri-training for Suggestion Mining
This paper describes our submission for the SemEval-2019 Suggestion Mining task.
