There are 10 repositories under unsupervised-domain-adaptation topic.
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
[CVPR22] Official Implementation of DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
[ICLR-2020] Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification.
PyTorch open-source toolbox for unsupervised or domain adaptive object re-ID.
pytorch implementation for Contrastive Adaptation Network
[ECCV22] Official Implementation of HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
[TKDD 2023] AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data
Implementation of ECCV 2020 paper "Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector"
Self-supervised Augmentation Consistency for Adapting Semantic Segmentation (CVPR 2021)
NeurIPS 2023: Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
Code for ICML2020 "Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation"
An official implementation of ICML 2022 paper "Learning Domain Adaptive Object Detection with Probabilistic Teacher"."
[AAAI 2024] Prompt-based Distribution Alignment for Unsupervised Domain Adaptation
Align and Distill: Unifying and Improving Domain Adaptive Object Detection (TMLR Featured 2025)
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night
Official Detectron2 implementation of DA-RetinaNet, An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites, Image and Vision Computing (IMAVIS) 2021
Deep learning research implemented on notebooks using PyTorch.
Detectron2 implementation of DA-Faster R-CNN, Domain Adaptive Faster R-CNN for Object Detection in the Wild, Computer Vision and Pattern Recognition (CVPR) 2018
Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling
Code and reuslts accompanying the NeurIPS 2022 paper with the title SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
This is a PyTorch implementation of the Unsupervised Domain Adaptation method proposed in the paper Deep CORAL: Correlation Alignment for Deep Domain Adaptation. Baochen Sun and Kate Saenko (ECCV 2016).
(RA-L 2022) See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation.
[JBHI2022] A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation
Code for paper "Object landmark discovery through unsupervised adaptation"
Adversarial Unsupervised Domain Adaptation for Acoustic Scene Classification
[ECCV 2022] The official repository of our paper "BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation"
[MICCAI 2021] Official Implementation for "MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels"
This repository contains code for the paper "Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation", published at IEEE JBHI 2022
Official Detectron2 implementation of STMDA-RetinaNet, A Multi Camera Unsupervised Domain Adaptation Pipeline for Object Detection in Cultural Sites through Adversarial Learning and Self-Training, Computer Vision and Image Understanding (CVIU) 2022
[TIP 2022] Pytorch implementation of "Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and Beyond"
Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learning, AAAI Conference on Artificial Intelligence (AAAI), 2022
Code for [MICCAI 2022] Domain Specific Convolution and High Frequency Reconstruction based Unsupervised Domain Adaptation for Medical Image Segmentation.
Source code for "Online Unsupervised Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions", ECCV 2022. This is the code has been implemented to perform training and evaluation of UDA approaches in continuous scenarios. The library has been implemented in PyTorch 1.7.1. Some newer versions should work as well.
Implementation of CLUDA: Contrastive learning in Unsupervised Domian Adaptation in Semantic Segmentation
A PyTorch implementation of AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation