Unsupervised Domain Adaptation
732 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 with no code
Domain Generalizable Person Search Using Unreal Dataset
Collecting and labeling real datasets to train the person search networks not only requires a lot of time and effort, but also accompanies privacy issues.
Parsing All Adverse Scenes: Severity-Aware Semantic Segmentation with Mask-Enhanced Cross-Domain Consistency
The SPM module incorporates a Severity Perception mechanism, guiding a Mask operation that enables our model to learn highly consistent features from the augmented scenes.
HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation
In this paper, we propose a novel hybrid pseudo-labeling framework for unsupervised event-based semantic segmentation, HPL-ESS, to alleviate the influence of noisy pseudo labels.
Adversarially Masked Video Consistency for Unsupervised Domain Adaptation
The second is a Masked Consistency Learning module to learn class-discriminative representations.
PCT: Perspective Cue Training Framework for Multi-Camera BEV Segmentation
In this work, we address these challenges by leveraging the abundance of unlabeled data available.
Semantics, Distortion, and Style Matter: Towards Source-free UDA for Panoramic Segmentation
However, the distinct projection discrepancies between source and target domains impede the direct knowledge transfer; thus, we propose a panoramic prototype adaptation module (PPAM) to integrate panoramic prototypes from the extracted knowledge for adaptation.
A Fourier Transform Framework for Domain Adaptation
By using unsupervised domain adaptation (UDA), knowledge can be transferred from a label-rich source domain to a target domain that contains relevant information but lacks labels.
CMDA: Cross-Modal and Domain Adversarial Adaptation for LiDAR-Based 3D Object Detection
Recent LiDAR-based 3D Object Detection (3DOD) methods show promising results, but they often do not generalize well to target domains outside the source (or training) data distribution.
Domain-Agnostic Mutual Prompting for Unsupervised Domain Adaptation
Specifically, the image contextual information is utilized to prompt the language branch in a domain-agnostic and instance-conditioned way.
DDF: A Novel Dual-Domain Image Fusion Strategy for Remote Sensing Image Semantic Segmentation with Unsupervised Domain Adaptation
Semantic segmentation of remote sensing images is a challenging and hot issue due to the large amount of unlabeled data.
