Unsupervised Domain Adaptation
727 papers with code • 36 benchmarks • 31 datasets
Unsupervised Domain Adaptation is a learning framework to transfer knowledge learned from source domains with a large number of annotated training examples to target domains with unlabeled data only.
Source: Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
Benchmarks
These leaderboards are used to track progress in Unsupervised Domain Adaptation
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Libraries
Use these libraries to find Unsupervised Domain Adaptation models and implementationsDatasets
Office-Home
Market-1501
DomainNet
Office-31
PACS
ImageNet-C
SYNTHIA
GTA5
ImageNet-R
ImageNet-A
Latest papers
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.
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.
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.
FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation
Adapting a medical image segmentation model to a new domain is important for improving its cross-domain transferability, and due to the expensive annotation process, Unsupervised Domain Adaptation (UDA) is appealing where only unlabeled images are needed for the adaptation.
Frequency Decomposition-Driven Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation
Cross-domain semantic segmentation of remote sensing (RS) imagery based on unsupervised domain adaptation (UDA) techniques has significantly advanced deep-learning applications in the geosciences.
DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation
State-of-the-art neural rankers pre-trained on large task-specific training data such as MS-MARCO, have been shown to exhibit strong performance on various ranking tasks without domain adaptation, also called zero-shot.
Cooperative Students: Navigating Unsupervised Domain Adaptation in Nighttime Object Detection
jichengyuan/cooperitive_students
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Unsupervised Domain Adaptation (UDA) has shown significant advancements in object detection under well-lit conditions; however, its performance degrades notably in low-visibility scenarios, especially at night, posing challenges not only for its adaptability in low signal-to-noise ratio (SNR) conditions but also for the reliability and efficiency of automated vehicles.
Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotation
x-coral/wda
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To address these issues, we investigate a highly annotation-efficient weak supervision, which assumes only sparse center-points on a small subset of object instances in the target training images.
Learning CNN on ViT: A Hybrid Model to Explicitly Class-specific Boundaries for Domain Adaptation
Compared to conventional DA methods, our ECB achieves superior performance, which verifies its effectiveness in this hybrid model.
CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning
Cuzyoung/CoDA
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SAVPT features a novel metric Severity that divides all adverse scene images into low-severity and high-severity images.
