Papers Semi-Supervised Domain Generalization
“Semi-Supervised Domain Generalization” 태그가 달린 논문 17편 · 필터 해제
FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization
Semi-supervised domain generalization (SSDG) aims to solve the problem of generalizing to out-of-distribution data when only a few labels are available. Due to label scarcity, applying domain generalization methods often…
BenchmarkingContrastive LearningDomain GeneralizationSelf-Supervised Learning+1Feature Modulation for Semi-Supervised Domain Generalization without Domain Labels
Semi-supervised domain generalization (SSDG) leverages a small fraction of labeled data alongside unlabeled data to enhance model generalization. Most of the existing SSDG methods rely on pseudo-labeling (PL) for unlabel…
Domain GeneralizationPseudo LabelSemi-Supervised Domain GeneralizationUnlocking the Potential of Unlabeled Data in Semi-Supervised Domain Generalization
We address the problem of semi-supervised domain generalization (SSDG), where the distributions of train and test data differ, and only a small amount of labeled data along with a larger amount of unlabeled data are avai…
Contrastive LearningDomain GeneralizationSemi-Supervised Domain GeneralizationCAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization
Domain Generalization (DG) seeks to transfer knowledge from multiple source domains to unseen target domains, even in the presence of domain shifts. Achieving effective generalization typically requires a large and diver…
Domain GeneralizationSemi-Supervised Domain GeneralizationDomain-Guided Weight Modulation for Semi-Supervised Domain Generalization
Unarguably, deep learning models capable of generalizing to unseen domain data while leveraging a few labels are of great practical significance due to low developmental costs. In search of this endeavor, we study the ch…
Domain GeneralizationSemi-Supervised Domain GeneralizationThe Devil is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable Semi-supervised Medical Image Segmentation
Despite the recent success of domain generalization in medical image segmentation, voxel-wise annotation for all source domains remains a huge burden. Semi-supervised domain generalization has been proposed very recently…
Domain GeneralizationImage SegmentationMedical Image SegmentationPseudo Label+3Towards Generalizing to Unseen Domains with Few Labels
We approach the challenge of addressing semi-supervised domain generalization (SSDG). Specifically, our aim is to obtain a model that learns domain-generalizable features by leveraging a limited subset of labelled data a…
Domain GeneralizationPseudo LabelSemi-Supervised Domain GeneralizationOvercoming Data Inequality across Domains with Semi-Supervised Domain Generalization
While there have been considerable advancements in machine learning driven by extensive datasets, a significant disparity still persists in the availability of data across various sources and populations. This inequality…
Domain GeneralizationSemi-Supervised Domain GeneralizationImproving Pseudo-labelling and Enhancing Robustness for Semi-Supervised Domain Generalization
Beyond attaining domain generalization (DG), visual recognition models should also be data-efficient during learning by leveraging limited labels. We study the problem of Semi-Supervised Domain Generalization (SSDG) whic…
Domain GeneralizationSemi-Supervised Domain GeneralizationTowards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
Volume-wise labeling in 3D medical images is a time-consuming task that requires expertise. As a result, there is growing interest in using semi-supervised learning (SSL) techniques to train models with limited labeled d…
Domain AdaptationDomain GeneralizationImage SegmentationMedical Image Segmentation+4Semi-Supervised Domain Generalization for Object Detection via Language-Guided Feature Alignment
Existing domain adaptation (DA) and generalization (DG) methods in object detection enforce feature alignment in the visual space but face challenges like object appearance variability and scene complexity, which make it…
DescriptiveDomain AdaptationDomain GeneralizationObject+3Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels
Developing a deep learning method for medical segmentation tasks heavily relies on a large amount of labeled data. However, the annotations require professional knowledge and are limited in number. Recently, semi-supervi…
Domain GeneralizationImage SegmentationSegmentationSemantic Segmentation+1MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization
Domain generalization (DG) aims at learning a model on source domains to well generalize on the unseen target domain. Although it has achieved great success, most of existing methods require the label information for all…
Domain GeneralizationMulti-Task LearningPseudo LabelSemi-Supervised Domain GeneralizationSemi-Supervised Domain Generalization with Evolving Intermediate Domain
Domain Generalization (DG) aims to generalize a model trained on multiple source domains to an unseen target domain. The source domains always require precise annotations, which can be cumbersome or even infeasible to ob…
Domain GeneralizationSemi-Supervised Domain GeneralizationBetter Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization
With the goal of directly generalizing trained model to unseen target domains, domain generalization (DG), a newly proposed learning paradigm, has attracted considerable attention. Previous DG models usually require a su…
Domain GeneralizationPseudo LabelSemi-Supervised Domain GeneralizationMixStyle Neural Networks for Domain Generalization and Adaptation
Neural networks do not generalize well to unseen data with domain shifts -- a longstanding problem in machine learning and AI. To overcome the problem, we propose MixStyle, a simple plug-and-play, parameter-free module t…
Data AugmentationDomain AdaptationDomain GeneralizationObject Recognition+5Semi-Supervised Domain Generalization with Stochastic StyleMatch
Ideally, visual learning algorithms should be generalizable, for dealing with any unseen domain shift when deployed in a new target environment; and data-efficient, for reducing development costs by using as little label…
Domain GeneralizationSemi-Supervised Domain Generalization