Unsupervised Domain Adaptation
734 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
Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation
We propose a novel approach for SF-OSDA that exploits the granularity of target-private categories by segregating their samples into multiple unknown classes.
Exploring selective image matching methods for zero-shot and few-sample unsupervised domain adaptation of urban canopy prediction
We explore simple methods for adapting a trained multi-task UNet which predicts canopy cover and height to a new geographic setting using remotely sensed data without the need of training a domain-adaptive classifier and extensive fine-tuning.
Search Beyond Queries: Training Smaller Language Models for Web Interactions via Reinforcement Learning
This work focuses on training smaller language models as agents across various scenarios, systematically evaluating the impact of human demonstrations on the training process.
Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation
A key challenge in panoptic UDA is reducing the domain gap between a labeled source and an unlabeled target domain while harmonizing the subtasks of semantic and instance segmentation to limit catastrophic interference.
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.
