Domain Adaptation
2005 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 with no code
Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications
Finally, we apply the proposed GCSD to two challenging machine learning tasks, namely deep learning-based clustering and the problem of multi-source domain adaptation.
Bridging the Synthetic-to-Authentic Gap: Distortion-Guided Unsupervised Domain Adaptation for Blind Image Quality Assessment
The annotation of blind image quality assessment (BIQA) is labor-intensive and time-consuming, especially for authentic images.
Cross-Modal Domain Adaptation in Brain Disease Diagnosis: Maximum Mean Discrepancy-based Convolutional Neural Networks
Such cross-modality adaptation is essential to improve the ability of models to consistently generalize across different imaging modalities.
Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-Training
Despite having diverse features important for generalization, the pre-trained feature extractor can overfit to the source data distribution during source training and forget relevant target domain knowledge.
A separability-based approach to quantifying generalization: which layer is best?
Which layers of a network are likely to generalize best?
Improving Domain Generalization on Gaze Estimation via Branch-out Auxiliary Regularization
Despite remarkable advancements, mainstream gaze estimation techniques, particularly appearance-based methods, often suffer from performance degradation in uncontrolled environments due to variations in illumination and individual facial attributes.
Adapting Pretrained Networks for Image Quality Assessment on High Dynamic Range Displays
Conventional image quality metrics (IQMs), such as PSNR and SSIM, are designed for perceptually uniform gamma-encoded pixel values and cannot be directly applied to perceptually non-uniform linear high-dynamic-range (HDR) colors.
A Self-explaining Neural Architecture for Generalizable Concept Learning
With the wide proliferation of Deep Neural Networks in high-stake applications, there is a growing demand for explainability behind their decision-making process.
More is Better: Deep Domain Adaptation with Multiple Sources
Domain adaptation (DA) aims to address this problem by aligning the distributions between the source and target domains.
Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection
Specifically, in the selection process, to improve the reliability of pseudo boxes, we propose a complementary augmentation strategy.
