Domain Adaptation
1989 papers with code • 54 benchmarks • 88 datasets
Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.
Further readings:
( Image credit: Unsupervised Image-to-Image Translation Networks )
Benchmarks
These leaderboards are used to track progress in Domain Adaptation
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Libraries
Use these libraries to find Domain Adaptation models and implementations
MNIST
SVHN
Office-Home
DomainNet
Office-31
PACS
SYNTHIA
USPS
GTA5
BDD100K
Subtasks
Latest papers
Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mapping
Thus, we proposed a simple yet effective framework to search for lightweight neural networks automatically for land cover mapping tasks under domain shifts.
A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition
The developed multi-source UDA theory is theoretical and the generalization error on target subject is guaranteed.
Unsupervised Domain Adaption Harnessing Vision-Language Pre-training
Wenlve-Zhou/VLP-UDA
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journal 2024
To address this, we propose a novel method called Cross-Modal Knowledge Distillation (CMKD), leveraging VLP models as teacher models to guide the learning process in the target domain, resulting in state-of-the-art performance.
Generalizable Face Landmarking Guided by Conditional Face Warping
plustwo0/generalized-face-landmarker
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Given a pair of real and stylized facial images, the conditional face warper predicts a warping field from the real face to the stylized one, in which the face landmarker predicts the ending points of the warping field and provides us with high-quality pseudo landmarks for the corresponding stylized facial images.
Learning from Unlabelled Data with Transformers: Domain Adaptation for Semantic Segmentation of High Resolution Aerial Images
esa-philab/learning_from_unlabeled_data_for_domain_adaptation_for_semantic_segmentation
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In this paper, we develop a new model for semantic segmentation of unlabelled images, the Non-annotated Earth Observation Semantic Segmentation (NEOS) model.
RanLayNet: A Dataset for Document Layout Detection used for Domain Adaptation and Generalization
midas-research/randomlaynet
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To solve this problem, domain adaptation approaches have been developed that use a small quantity of labeled data to adjust the model to the target domain.
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation
mqinghe/midss
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To fully utilize the information within the intermediate domain, we propose a symmetric Guidance training strategy (SymGD), which additionally offers direct guidance to unlabeled data by merging pseudo labels from intermediate samples.
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
openbmb/minicpm
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For data scaling, we introduce a Warmup-Stable-Decay (WSD) learning rate scheduler (LRS), conducive to continuous training and domain adaptation.
Anatomical Conditioning for Contrastive Unpaired Image-to-Image Translation of Optical Coherence Tomography Images
For a unified analysis of medical images from different modalities, data harmonization using image-to-image (I2I) translation is desired.
Self-Labeling in Multivariate Causality and Quantification for Adaptive Machine Learning
Several unanswered research questions remain, including self-labeling's compatibility with multivariate causality and the quantitative analysis of the auxiliary models used in the self-labeling.
