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# awesome-domain-adaptation

[![MIT License](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![MIT License](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT)

This repo is a collection of AWESOME things about domain adaptation, including papers, code, etc. Feel free to star and fork.

Expand Down Expand Up @@ -157,7 +157,7 @@ for Adversarial Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/con
- Domain-Symmetric Networks for Adversarial Domain Adaptation [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Domain-Symmetric_Networks_for_Adversarial_Domain_Adaptation_CVPR_2019_paper.pdf) [[Pytorch]](https://github.com/YBZh/SymNets)
- DLOW: Domain Flow for Adaptation and Generalization [[CVPR2019 Oral]](https://arxiv.org/pdf/1812.05418.pdf)
- Progressive Feature Alignment for Unsupervised Domain Adaptation [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Progressive_Feature_Alignment_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf) [[Tensorflow]](https://github.com/Xiewp/PFAN)
- Gotta Adapt ’Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Tran_Gotta_Adapt_Em_All_Joint_Pixel_and_Feature-Level_Domain_Adaptation_CVPR_2019_paper.pdf)
- Gotta Adapt ’Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild [[CVPR2019]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Tran_Gotta_Adapt_Em_All_Joint_Pixel_and_Feature-Level_Domain_Adaptation_CVPR_2019_paper.pdf)
- Looking back at Labels: A Class based Domain Adaptation Technique [[IJCNN2019]](https://arxiv.org/abs/1904.01341) [[Project]](https://vinodkkurmi.github.io/DiscriminatorDomainAdaptation/)
- Consensus Adversarial Domain Adaptation [[AAAI2019]](https://aaai.org/ojs/index.php/AAAI/article/view/4552)
- Transferable Attention for Domain Adaptation [[AAAI2019]](http://ise.thss.tsinghua.edu.cn/~mlong/doc/transferable-attention-aaai19.pdf)
Expand All @@ -176,7 +176,7 @@ for Adversarial Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/con
- Generate To Adapt: Aligning Domains using Generative Adversarial Networks [[CVPR2018]](https://arxiv.org/abs/1704.01705) [[Pytorch(Official)]](https://github.com/yogeshbalaji/Generate_To_Adapt)
- Image to Image Translation for Domain Adaptation [[CVPR2018]](https://arxiv.org/abs/1712.00479)
- Unsupervised Domain Adaptation with Similarity Learning [[CVPR2018]](https://arxiv.org/abs/1711.08995)
- Conditional Generative Adversarial Network for Structured Domain Adaptation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Hong_Conditional_Generative_Adversarial_CVPR_2018_paper.pdf)
- Conditional Generative Adversarial Network for Structured Domain Adaptation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Hong_Conditional_Generative_Adversarial_CVPR_2018_paper.pdf)
- Collaborative and Adversarial Network for Unsupervised Domain Adaptation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Collaborative_and_Adversarial_CVPR_2018_paper.pdf) [[Pytorch]](https://github.com/zhangweichen2006/iCAN)
- Re-Weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Re-Weighted_Adversarial_Adaptation_CVPR_2018_paper.pdf)
- Multi-Adversarial Domain Adaptation [[AAAI2018]](http://ise.thss.tsinghua.edu.cn/~mlong/doc/multi-adversarial-domain-adaptation-aaai18.pdf) [[Caffe(Official)]](https://github.com/thuml/MADA)
Expand Down Expand Up @@ -255,7 +255,7 @@ Label Shift CO-ALignment [[23 Oct 2019]](https://arxiv.org/abs/1910.10320)
- Global-Local Regularization Via Distributional Robustness [[AISTATS2023]](https://arxiv.org/abs/2203.00553) [[Pytorch]](https://github.com/VietHoang1512/GLOT/)
- MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning [[UAI2021]](https://auai.org/uai2021/pdf/uai2021.106.pdf)
- LAMDA: Label Matching Deep Domain Adaptation [[ICML2021]](http://proceedings.mlr.press/v139/le21a.html)
- TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport [[IJCAI2021]](https://www.ijcai.org/proceedings/2021/0394.pdf)
- TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport [[IJCAI2021]](https://www.ijcai.org/proceedings/2021/0394.pdf)
- Unbalanced minibatch Optimal Transport; applications to Domain Adaptation [[ICML2021]](https://arxiv.org/abs/2103.03606) [[Pytorch]](https://github.com/kilianFatras/JUMBOT)
- Graph Optimal Transport for Cross-Domain Alignment [[ICML2020]](https://proceedings.icml.cc/static/paper_files/icml/2020/971-Paper.pdf)
- Margin-aware Adversarial Domain Adaptation with Optimal Transport [[ICML2020]](https://proceedings.icml.cc/static/paper_files/icml/2020/2666-Paper.pdf) [[code]](https://github.com/sofiendhouib/MADAOT)
Expand Down Expand Up @@ -313,7 +313,7 @@ Label Shift CO-ALignment [[23 Oct 2019]](https://arxiv.org/abs/1910.10320)
- Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/90cc440b1b8caa520c562ac4e4bbcb51-Abstract.html)
- SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Prabhu_SENTRY_Selective_Entropy_Optimization_via_Committee_Consistency_for_Unsupervised_Domain_ICCV_2021_paper.html)
- Transporting Causal Mechanisms for Unsupervised Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Yue_Transporting_Causal_Mechanisms_for_Unsupervised_Domain_Adaptation_ICCV_2021_paper.html)
- Semantic Concentration for Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Li_Semantic_Concentration_for_Domain_Adaptation_ICCV_2021_paper.html)
- Semantic Concentration for Domain Adaptation [[ICCV2021]](https://openaccess.thecvf.com/content/ICCV2021/html/Li_Semantic_Concentration_for_Domain_Adaptation_ICCV_2021_paper.html)
- FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Na_FixBi_Bridging_Domain_Spaces_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf)
- Domain Adaptation With Auxiliary Target Domain-Oriented Classifier [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Liang_Domain_Adaptation_With_Auxiliary_Target_Domain-Oriented_Classifier_CVPR_2021_paper.pdf)
- Conditional Bures Metric for Domain Adaptation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Conditional_Bures_Metric_for_Domain_Adaptation_CVPR_2021_paper.pdf)
Expand Down Expand Up @@ -392,7 +392,7 @@ Decomposition [[25 Sep 2019]](https://arxiv.org/abs/1909.11285)
- Virtual Mixup Training for Unsupervised Domain Adaptation [[arXiv on 24 May 2019]](https://arxiv.org/abs/1905.04215) [[Tensorflow]](https://github.com/xudonmao/VMT)
- Learning Smooth Representation for Unsupervised Domain Adaptation [[arXiv 26 May 2019]](https://arxiv.org/abs/1905.10748v1)
- Towards Self-similarity Consistency and Feature Discrimination for Unsupervised Domain Adaptation [[arXiv 13 Apr 2019]](https://arxiv.org/abs/1904.06490v1)
- Easy Transfer Learning By Exploiting Intra-domain Structures [[arXiv 2 Apr 2019]](https://arxiv.org/abs/1904.01376v1)
- Easy Transfer Learning By Exploiting Intra-domain Structures [[arXiv 2 Apr 2019]](https://arxiv.org/abs/1904.01376v1)
- Domain Discrepancy Measure Using Complex Models in Unsupervised Domain Adaptation [[arXiv 30 Jan 2019]](https://arxiv.org/abs/1901.10654v1)
- Domain Alignment with Triplets [[arXiv 22 Jan 2019]](https://arxiv.org/abs/1812.00893v2)
- Deep Discriminative Learning for Unsupervised Domain Adaptation [[arXiv 17 Nov 2018]](https://arxiv.org/abs/1811.07134v1)
Expand Down Expand Up @@ -725,6 +725,7 @@ Decomposition [[25 Sep 2019]](https://arxiv.org/abs/1909.11285)
- PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization [[ICCV2023]](https://arxiv.org/abs/2307.15199) [[Project]](https://promptstyler.github.io/)
- Sparse Mixture-of-Experts are Domain Generalizable Learners [[ICLR2023(Oral)]](https://openreview.net/forum?id=RecZ9nB9Q4) [[Pytorch]](https://github.com/Luodian/Generalizable-Mixture-of-Experts)
- Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts [[NeruIPS2022]](https://arxiv.org/pdf/2210.03885.pdf) [[Pytorch]](https://github.com/n3il666/Meta-DMoE)
- Cross-Domain Ensemble Distillation for Domain Generalized Semantic Segmentation [[ECCV 2022]](https://arxiv.org/pdf/2211.14058) [[Pytorch]](https://github.com/leekyungmoon/XDED)
- Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation [[ECCV 2022]](https://arxiv.org/pdf/2204.02548.pdf) [[Pytorch]](https://github.com/HeliosZhao/SHADE)
- Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification [[CVPR 2021]](https://arxiv.org/pdf/2012.00417.pdf) [[Pytorch]](https://github.com/HeliosZhao/M3L)
- Domain Generalization via Inference-time Label-Preserving Target Projections [[CVPR2021]](https://arxiv.org/abs/2103.01134) [[Pytorch]](https://github.com/peterDan8/InferenceTimeDG)
Expand Down Expand Up @@ -793,7 +794,7 @@ Decomposition [[25 Sep 2019]](https://arxiv.org/abs/1909.11285)

**Survey**
- Unsupervised Domain Adaptation of Object Detectors: A Survey [[Arxiv 27 May 2021]](https://arxiv.org/abs/2105.13502)

**Conference**
- Improving Object Detection via Local-Global Contrastive Learning [[BMVC2024]](https://arxiv.org/abs/2410.05058) [[Project]](https://local-global-detection.github.io/)
- Supervision Interpolation via LossMix: Generalizing Mixup for Object Detection and Beyond [[AAAI2024]](https://arxiv.org/abs/2303.10343)
Expand Down Expand Up @@ -901,7 +902,7 @@ for Cross-dataset 3D Object Detection [[CVPR2021]](https://openaccess.thecvf.com
- Cross-View Regularization for Domain Adaptive Panoptic Segmentation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Cross-View_Regularization_for_Domain_Adaptive_Panoptic_Segmentation_CVPR_2021_paper.pdf)
- Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation [[CVPR2021]](https://arxiv.org/abs/2103.04705v1)
- MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_MetaCorrection_Domain-Aware_Meta_Loss_Correction_for_Unsupervised_Domain_Adaptation_in_CVPR_2021_paper.pdf)
- Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_Coarse-To-Fine_Domain_Adaptive_Semantic_Segmentation_With_Photometric_Alignment_and_Category-Center_CVPR_2021_paper.pdf)
- Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_Coarse-To-Fine_Domain_Adaptive_Semantic_Segmentation_With_Photometric_Alignment_and_Category-Center_CVPR_2021_paper.pdf)
- Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation [[CVPR2021]](https://arxiv.org/abs/2103.04717v2)
- Source-Free Domain Adaptation for Semantic Segmentation [[CVPR2021]](https://arxiv.org/abs/2103.16372v1)
- Instance Adaptive Self-Training for Unsupervised Domain Adaptation [[ECCV 2020]](https://arxiv.org/abs/2008.12197) [[Pytorch]](https://github.com/bupt-ai-cz/IAST-ECCV2020)
Expand All @@ -913,7 +914,7 @@ for Cross-dataset 3D Object Detection [[CVPR2021]](https://openaccess.thecvf.com
- Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670290.pdf)
- Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720477.pdf)
- Unsupervised Domain Adaptation for Semantic Segmentation of NIR Images through Generative Latent Search [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510409.pdf)
- Domain Adaptive Semantic Segmentation Using Weak Labels [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540545.pdf)
- Domain Adaptive Semantic Segmentation Using Weak Labels [[ECCV2020]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540545.pdf)
- Content-Consistent Matching for Domain Adaptive Semantic Segmentation [[ECCV2020]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590426.pdf) [[PyTorch]](https://github.com/Solacex/CCM)
- Cross-Domain Semantic Segmentation via Domain-Invariant Interactive Relation Transfer [[CVPR2020]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Lv_Cross-Domain_Semantic_Segmentation_via_Domain-Invariant_Interactive_Relation_Transfer_CVPR_2020_paper.pdf)
- Phase Consistent Ecological Domain Adaptation [[CVPR2020]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_Phase_Consistent_Ecological_Domain_Adaptation_CVPR_2020_paper.pdf) [[Pytorch]](https://github.com/donglao/PCEDA)
Expand Down Expand Up @@ -951,7 +952,7 @@ Segmentation: A Non-Adversarial Approach [[ICCV2019]](http://openaccess.thecvf.c
- Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Xinge_Zhu_Penalizing_Top_Performers_ECCV_2018_paper.pdf)
- Domain transfer through deep activation matching [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Haoshuo_Huang_Domain_transfer_through_ECCV_2018_paper.pdf)
- Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Yang_Zou_Unsupervised_Domain_Adaptation_ECCV_2018_paper.pdf) [[Pytorch]](https://github.com/yzou2/CBST)
- DCAN: Dual channel-wise alignment networks for unsupervised scene adaptation [[ECCV2018]](https://eccv2018.org/openaccess/content_ECCV_2018/papers/Zuxuan_Wu_DCAN_Dual_Channel-wise_ECCV_2018_paper.pdf)
- DCAN: Dual channel-wise alignment networks for unsupervised scene adaptation [[ECCV2018]](https://eccv2018.org/openaccess/content_ECCV_2018/papers/Zuxuan_Wu_DCAN_Dual_Channel-wise_ECCV_2018_paper.pdf)
- Fully convolutional adaptation networks for semantic
segmentation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Fully_Convolutional_Adaptation_CVPR_2018_paper.pdf)
- Learning to Adapt Structured Output Space for Semantic Segmentation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Tsai_Learning_to_Adapt_CVPR_2018_paper.pdf) [[Pytorch]](https://github.com/wasidennis/AdaptSegNet)
Expand All @@ -966,7 +967,7 @@ segmentation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/
- Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation [[IJCV2020]](https://arxiv.org/abs/2003.03773)[[Pytorch]](https://github.com/layumi/Seg-Uncertainty)
- Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet [[Neurocomputing 2021]](https://arxiv.org/abs/2006.15954) [[Pytorch]](https://github.com/bupt-ai-cz/CAC-UNet-DigestPath2019)
- Affinity Space Adaptation for Semantic Segmentation Across Domains [[TIP2020]](https://arxiv.org/abs/2009.12559)[[Pytorch]](https://github.com/idealwei/ASANet)
- Semantic-aware short path adversarial training for cross-domain semantic segmentation [[Neurocomputing 2019]](https://www.sciencedirect.com/science/article/pii/S0925231219315656#fig0002)
- Semantic-aware short path adversarial training for cross-domain semantic segmentation [[Neurocomputing 2019]](https://www.sciencedirect.com/science/article/pii/S0925231219315656#fig0002)
- Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes [[TIP]](https://arxiv.org/abs/1904.09092v1)

**Arxiv**
Expand Down Expand Up @@ -998,7 +999,7 @@ segmentation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/
- A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification [[ICCV2019]](https://arxiv.org/abs/1904.03425)
- Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification [[CVPR2019]](https://arxiv.org/abs/1904.01990v1) [[Pytorch]](https://github.com/zhunzhong07/ECN)
- Domain Adaptation through Synthesis for Unsupervised Person Re-identification [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Slawomir_Bak_Domain_Adaptation_through_ECCV_2018_paper.pdf)
- Person Transfer GAN to Bridge Domain Gap for Person Re-Identification [[CVPR2018]](https://arxiv.org/abs/1711.08565v2)
- Person Transfer GAN to Bridge Domain Gap for Person Re-Identification [[CVPR2018]](https://arxiv.org/abs/1711.08565v2)
- Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification [[CVPR2018]](https://arxiv.org/abs/1711.07027v3)

**Arxiv**
Expand Down Expand Up @@ -1048,7 +1049,7 @@ segmentation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/

**Journal**
- Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet [[Neurocomputing 2021]](https://arxiv.org/abs/2006.15954) [[Pytorch]](https://github.com/bupt-ai-cz/CAC-UNet-DigestPath2019)

**Arxiv**
- On-the-Fly Test-time Adaptation for Medical Image Segmentation [[10 Mar 2022]](https://arxiv.org/abs/2203.05574) [[Pytorch]](https://github.com/jeya-maria-jose/On-The-Fly-Adaptation)
- Target and task specific source-free domain adaptive image segmentation [[10 Mar 2023]](https://arxiv.org/abs/2203.15792) [[Pytorch]](https://github.com/Vibashan/tt-sfuda)
Expand Down Expand Up @@ -1136,7 +1137,7 @@ segmentation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/papers/
- Cross-domain fault diagnosis through optimal transport for a CSTR process [[DYCOPS2022]](https://www.sciencedirect.com/science/article/pii/S2405896322009727) [[Code]](https://github.com/eddardd/CrossDomainFaultDiagnosis)

**Journal**
- DASGIL: Domain Adaptation for Semantic and Geometric-Aware Image-Based Localization [[TIP2020]](https://ieeexplore.ieee.org/document/9296559) [[Pytorch]](https://github.com/HanjiangHu/DASGIL)
- DASGIL: Domain Adaptation for Semantic and Geometric-Aware Image-Based Localization [[TIP2020]](https://ieeexplore.ieee.org/document/9296559) [[Pytorch]](https://github.com/HanjiangHu/DASGIL)
- An Unsupervised Domain Adaptation Scheme for Single-Stage Artwork Recognition in Cultural Sites [[Image and Vision Computing 2020]](https://arxiv.org/abs/2008.01882v3) [[Pytorch]](https://github.com/fpv-iplab/DA-RetinaNet) [[Project]](https://iplab.dmi.unict.it/EGO-CH-OBJ-UDA/)
- Multi-source transfer learning of time series in cyclical manufacturing [[JIntellManuf2020]](https://link.springer.com/article/10.1007/s10845-019-01499-4)
- Domain adaptation for regression under Beer-Lambert's law [[KBS2020]](https://www.sciencedirect.com/science/article/abs/pii/S0950705120305761)
Expand Down