【资源】领域自适应相关论文、代码分享

【导读】领域自适应（Domain Adaptation）是迁移学习（Transfer Learning）的一种，思路是将不同领域（如两个不同的数据集）的数据特征映射到同一个特征空间，这样可利用其它领域数据来增强目标领域训练。本文整理了该领域的一些论文、代码、应用等。

目录

• Papers

• Object Detection

• Semantic Segmentation

• Person Re-identification

• Video Domain Adaptation

• Medical Related

• Monocular Depth Estimation

• Others

• Domain Generalization

• Domain Randomization

• Meta-Learning

• Transfer Metric Learning

• Others

• Adversarial Methods

• Distance-based Methods

• Optimal Transport

• Incremental Methods

• Other Methods

• Survey

• Theory

• Unsupervised DA

• Semi-supervised DA

• Weakly-Supervised DA

• Zero-shot DA

• One-shot DA

• Few-shot DA

• Image-to-Image Translation

• Disentangled Representation Learning

• Open Set DA

• Partial DA

• Multi Source DA

• Multi Target DA

• Multi Step DA

• Heterogeneous DA

• Target-agnostic DA

• Source-agnostic DA

• Model Selection

• General Transfer Learning

• Applications

• Benchmarks

• Library

• Other Resources

Papers

Survey

• Transfer Adaptation Learning: A Decade Survey [[arXiv 12 Mar 2019]] https://arxiv.org/abs/1903.04687

• A review of single-source unsupervised domain adaptation [[arXiv 16 Jan 2019]] https://arxiv.org/abs/1901.05335

• An introduction to domain adaptation and transfer learning [[arXiv 31 Dec 2018]] https://arxiv.org/abs/1812.11806v2

• A Survey of Unsupervised Deep Domain Adaptation [[arXiv 6 Dec 2018]] https://arxiv.org/abs/1812.02849v2

• A Survey on Deep Transfer Learning [[ICANN2018]] https://arxiv.org/abs/1808.01974v1

• Deep Visual Domain Adaptation: A Survey [[arXiv 2018]] https://arxiv.org/abs/1802.03601v4

• Transfer Learning for Cross-Dataset Recognition: A Survey [[arXiv 2017]] https://sci-hub.tw/https://arxiv.org/abs/1705.04396

• Domain Adaptation for Visual Applications: A Comprehensive Survey [[arXiv 2017]] https://arxiv.org/abs/1702.05374

• Visual domain adaptation: A survey of recent advances [[2015]] https://sci-hub.tw/10.1109/msp.2014.2347059

Theory

Arxiv

• A General Upper Bound for Unsupervised Domain Adaptation [[3 Oct 2019]] https://arxiv.org/abs/1910.01409

• On Deep Domain Adaptation: Some Theoretical Understandings [[arXiv 15 Nov 2018]] https://arxiv.org/abs/1811.06199

Conference

• Bridging Theory and Algorithm for Domain Adaptation [[ICML2019]] http://proceedings.mlr.press/v97/zhang19i/zhang19i.pdf  [[Pytorch]] https://github.com/thuml/MDD

• On Learning Invariant Representation for Domain Adaptation [[ICML2019]] https://arxiv.org/abs/1901.09453v1  [[code]] https://github.com/KeiraZhao/On-Learning-Invariant-Representations-for-Domain-Adaptation

• Learning Bounds for Domain Adaptation [[NIPS2007]] http://papers.nips.cc/paper/3212-learning-bounds-for-domain-adaptation

• Analysis of Representations for Domain Adaptation [[NIPS2006]] https://papers.nips.cc/paper/2983-analysis-of-representations-for-domain-adaptation

Journal

• A theory of learning from different domains [[ML2010]] https://link.springer.com/content/pdf/10.1007%2Fs10994-009-5152-4.pdf

Unsupervised DA

Adversarial Methods

Arxiv

• Adversarial Variational Domain Adaptation [[25 Sep 2019]] https://arxiv.org/abs/1909.11651

• Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation [[arXiv 13 Sep 2019]] https://arxiv.org/abs/1909.05288

• SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation [[arXiv 11 Jun 2019]] https://arxiv.org/abs/1906.04338v1

• Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation [[arXiv 10 Jun 2019]] https://arxiv.org/abs/1906.04053v1

• Adversarial Domain Adaptation Being Aware of Class Relationships [[arXiv 28 May 2019]] https://arxiv.org/abs/1905.11931v1

• Domain-Invariant Adversarial Learning for Unsupervised Domain Adaption [[arXiv 30 Nov 2018]] https://arxiv.org/abs/1811.12751

• Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks [[arXiv 17 Feb 2019]] https://arxiv.org/abs/1902.06328v1

• DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification [[arXiv 30 Dec 2018]] https://arxiv.org/abs/1812.11478

• Unsupervised Domain Adaptation using Generative Models and Self-ensembling [[arXiv 2 Dec 2018]] https://arxiv.org/abs/1812.00479

• Domain Confusion with Self Ensembling for Unsupervised Adaptation [[arXiv 10 Oct 2018]] https://arxiv.org/abs/1810.04472

• Improving Adversarial Discriminative Domain Adaptation [[arXiv 10 Sep 2018]] https://arxiv.org/abs/1809.03625

• M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning [[arXiv 6 Jul 2018]] https://arxiv.org/abs/1807.02552v1  [[Pytorch official ]] https://github.com/IssamLaradji/M-ADDA

• Factorized Adversarial Networks for Unsupervised Domain Adaptation [[arXiv 4 Jun 2018]] https://arxiv.org/abs/1806.01376v1

• DiDA: Disentangled Synthesis for Domain Adaptation [[arXiv 21 May 2018]] https://arxiv.org/abs/1805.08019v1

• Unsupervised Domain Adaptation with Adversarial Residual Transform Networks [[arXiv 25 Apr 2018]] https://arxiv.org/abs/1804.09578

• Causal Generative Domain Adaptation Networks [[arXiv 28 Jun 2018]] https://arxiv.org/abs/1804.04333v3

Conference

• Transfer Learning with Dynamic Adversarial Adaptation Network [[ICDM2019]] https://arxiv.org/abs/1909.08184

• Cycle-consistent Conditional Adversarial Transfer Networks [[ACM MM2019]] https://arxiv.org/abs/1909.07618  [[Pytorch]] https://github.com/lijin118/3CATN

• Learning Disentangled Semantic Representation for Domain Adaptation [[IJCAI2019]] https://www.ijcai.org/proceedings/2019/0285.pdf

• Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation [[ICML2019]] http://proceedings.mlr.press/v97/chen19i/chen19i.pdf  [[Pytorch]] https://github.com/thuml/Batch-Spectral-Penalization

• Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers [[ICML2019]] http://proceedings.mlr.press/v97/liu19b/liu19b.pdf  [[Pytorch]] https://github.com/thuml/Transferable-Adversarial-Training

• Cluster Alignment with a Teacher for Unsupervised Domain Adaptation [[ICCV2019]] https://arxiv.org/abs/1903.09980v1  [[Tensorflow]] https://github.com/thudzj/CAT

• Unsupervised Domain Adaptation via Regularized Conditional Alignment [[ICCV2019]] https://arxiv.org/abs/1905.10885v1

• Attending to Discriminative Certainty for Domain Adaptation [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Kurmi_Attending_to_Discriminative_Certainty_for_Domain_Adaptation_CVPR_2019_paper.pdf  [[Project]] https://delta-lab-iitk.github.io/CADA/

• Universal Domain Adaptation [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/You_Universal_Domain_Adaptation_CVPR_2019_paper.pdf  [[Pytorch]] https://github.com/thuml/Universal-Domain-Adaptation

• GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Ma_GCAN_Graph_Convolutional_Adversarial_Network_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf

• 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

• 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/Papers/AAAI/2019/AAAI-ZouH.697.pdf

• Transferable Attention for Domain Adaptation [[AAAI2019]] http://ise.thss.tsinghua.edu.cn/~mlong/doc/transferable-attention-aaai19.pdf

• Exploiting Local Feature Patterns for Unsupervised Domain Adaptation [[AAAI2019]] https://arxiv.org/abs/1811.05042v2

• Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation [[ICLR2019]] https://openreview.net/forum?id=B1G9doA9F7

• Conditional Adversarial Domain Adaptation [[NIPS2018]] http://papers.nips.cc/paper/7436-conditional-adversarial-domain-adaptation  [[Pytorch official ]] https://github.com/thuml/CDAN  [[Pytorch third party ]] https://github.com/thuml/CDAN

• Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Baris_Gecer_Semi-supervised_Adversarial_Learning_ECCV_2018_paper.pdf

• Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: the Benefit of Target Expectation Maximization [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Guoliang_Kang_Deep_Adversarial_Attention_ECCV_2018_paper.pdf

• Learning Semantic Representations for Unsupervised Domain Adaptation [[ICML2018]] http://proceedings.mlr.press/v80/xie18c.html  [[TensorFlow Official ]] https://github.com/Mid-Push/Moving-Semantic-Transfer-Network

• CyCADA: Cycle-Consistent Adversarial Domain Adaptation [[ICML2018]] http://proceedings.mlr.press/v80/hoffman18a.html  [[Pytorch official ]] https://github.com/jhoffman/cycada_release

• From source to target and back: Symmetric Bi-Directional Adaptive GAN [[CVPR2018]] http://openaccess.thecvf.com/content_cvpr_2018/papers/Russo_From_Source_to_CVPR_2018_paper.pdf  [[Keras Official ]] https://github.com/engharat/SBADAGAN  [[Pytorch]] https://github.com/naoto0804/pytorch-SBADA-GAN

• Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation [[CVPR2018]] http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Detach_and_Adapt_CVPR_2018_paper.pdf  [[Tensorflow]] https://github.com/ycliu93/CDRD

• Maximum Classifier Discrepancy for Unsupervised Domain Adaptation [[CVPR2018]] http://openaccess.thecvf.com/content_cvpr_2018/papers/Saito_Maximum_Classifier_Discrepancy_CVPR_2018_paper.pdf  [[Pytorch Official ]] https://github.com/mil-tokyo/MCD_DA

• Adversarial Feature Augmentation for Unsupervised Domain Adaptation [[CVPR2018]] https://arxiv.org/abs/1711.08561  [[TensorFlow Official ]] https://github.com/ricvolpi/adversarial-feature-augmentation

• Duplex Generative Adversarial Network for Unsupervised Domain Adaptation [[CVPR2018]] http://vipl.ict.ac.cn/uploadfile/upload/2018041610083083.pdf  [[Pytorch Official ]] http://vipl.ict.ac.cn/view_database.php?id=6

• 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

• 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

• Wasserstein Distance Guided Representation Learning for Domain Adaptation [[AAAI2018]] https://arxiv.org/abs/1707.01217  [[TensorFlow official ]] https://github.com/RockySJ/WDGRL  [[Pytorch]] https://github.com/jvanvugt/pytorch-domain-adaptation

• Incremental Adversarial Domain Adaptation for Continually Changing Environments [[ICRA2018]] https://arxiv.org/abs/1712.07436

• Adversarial Dropout Regularization [[ICLR2018]] https://openreview.net/forum?id=HJIoJWZCZ

• A DIRT-T Approach to Unsupervised Domain Adaptation [[ICLR2018 Poster]] https://openreview.net/forum?id=H1q-TM-AW  [[Tensorflow Official ]] https://github.com/RuiShu/dirt-t

• Label Efficient Learning of Transferable Representations acrosss Domains and Tasks [[NIPS2017]] http://vision.stanford.edu/pdf/luo2017nips.pdf  [[Project]] http://alan.vision/nips17_website/

• Adversarial Discriminative Domain Adaptation [[CVPR2017]] http://openaccess.thecvf.com/content_cvpr_2017/papers/Tzeng_Adversarial_Discriminative_Domain_CVPR_2017_paper.pdf  [[Tensorflow Official ]] https://github.com/erictzeng/adda  [[Pytorch]] https://github.com/corenel/pytorch-adda

• Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks [[CVPR2017]] http://openaccess.thecvf.com/content_cvpr_2017/papers/Bousmalis_Unsupervised_Pixel-Level_Domain_CVPR_2017_paper.pdf  [[Tensorflow Official ]] https://github.com/tensorflow/models/tree/master/research/domain_adaptation  [[Pytorch]] https://github.com/vaibhavnaagar/pixelDA_GAN

• Domain Separation Networks [[NIPS2016]] http://papers.nips.cc/paper/6254-domain-separation-networks

• Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation [[ECCV2016]] https://arxiv.org/abs/1607.03516

• Domain-Adversarial Training of Neural Networks [[JMLR2016]] http://www.jmlr.org/papers/volume17/15-239/15-239.pdf

• Unsupervised Domain Adaptation by Backpropagation [[ICML2015]] http://proceedings.mlr.press/v37/ganin15.pdf  [[Caffe Official ]] https://github.com/ddtm/caffe/tree/grl  [[Tensorflow]] https://github.com/shucunt/domain_adaptation  [[Pytorch]] https://github.com/fungtion/DANN

Journal

• TarGAN: Generating target data with class labels for unsupervised domain adaptation [[Knowledge-Based Systems]] https://github.com/zhaoxin94/awsome-domain-adaptation/blob/master

Distance-based Methods

Arxiv

• Deep Domain Confusion: Maximizing for Domain Invariance [[Arxiv 2014]] https://arxiv.org/abs/1412.3474

Conference

• Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation [[AAAI2019]] https://arxiv.org/abs/1808.09347v2

• Residual Parameter Transfer for Deep Domain Adaptation [[CVPR2018]] https://arxiv.org/abs/1711.07714

• Deep Asymmetric Transfer Network for Unbalanced Domain Adaptation [[AAAI2018]] http://media.cs.tsinghua.edu.cn/~multimedia/cuipeng/papers/DATN.pdf

• Deep CORAL: Correlation Alignment for Deep Domain Adaptation [[ECCV2016]] https://arxiv.org/abs/1607.01719

Optimal Transport

• CDOT: Continuous Domain Adaptation using Optimal Transport [[20 Sep 2019]] https://arxiv.org/abs/1909.11448

• Differentially Private Optimal Transport: Application to Domain Adaptation [[IJCAI]] https://www.ijcai.org/proceedings/2019/0395.pdf

• DeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Bharath_Bhushan_Damodaran_DeepJDOT_Deep_Joint_ECCV_2018_paper.pdf  [[Keras]] https://github.com/bbdamodaran/deepJDOT

• Joint Distribution Optimal Transportation for Domain Adaptation [[NIPS2017]] http://papers.nips.cc/paper/6963-joint-distribution-optimal-transportation-for-domain-adaptation.pdf  [[python]] https://github.com/rflamary/JDOT  [[Python Optimal Transport Library]] https://github.com/rflamary/POT

Incremental Methods

• Incremental Adversarial Domain Adaptation for Continually Changing Environments [[ICRA2018]] https://arxiv.org/abs/1712.07436

• Continuous Manifold based Adaptation for Evolving Visual Domains [[CVPR2014]] https://people.eecs.berkeley.edu/~jhoffman/papers/Hoffman_CVPR2014.pdf

Other Methods

Arxiv

• Domain-invariant Learning using Adaptive Filter Decomposition [[25 Sep 2019]] https://arxiv.org/abs/1909.11285

• Discriminative Clustering for Robust Unsupervised Domain Adaptation [[arXiv 30 May 2019]] https://arxiv.org/abs/1905.13331

• 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

• Switchable Whitening for Deep Representation Learning [[arXiv 22 Apr 2019]] https://arxiv.org/abs/1904.09739

• 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

• 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

Conference

• CUDA: Contradistinguisher for Unsupervised Domain Adaptation [[ICDM2019]] https://arxiv.org/abs/1909.03442

• Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment [[ICML2019]] http://proceedings.mlr.press/v97/wu19f/wu19f.pdf

• Confidence Regularized Self-Training [[ICCV2019 Oral]] https://arxiv.org/pdf/1908.09822.pdf  [[Pytorch]] https://github.com/yzou2/CRST

• Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation [[ICCV2019]] https://arxiv.org/abs/1811.07456  [[Pytorch official ]] https://github.com/jihanyang/AFN

• Transferrable Prototypical Networks for Unsupervised Domain Adaptation [[CVPR2019 Oral ]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Pan_Transferrable_Prototypical_Networks_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf

• Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Lee_Sliced_Wasserstein_Discrepancy_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf

• Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss [[CVPR 2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Roy_Unsupervised_Domain_Adaptation_Using_Feature-Whitening_and_Consensus_Loss_CVPR_2019_paper.pdf  [[Pytorch]] https://github.com/roysubhankar/dwt-domain-adaptation

• Domain Specific Batch Normalization for Unsupervised Domain Adaptation [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Chang_Domain-Specific_Batch_Normalization_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf

• AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Mancini_AdaGraph_Unifying_Predictive_and_Continuous_Domain_Adaptation_Through_Graphs_CVPR_2019_paper.pdf  [[Pytorch]] https://github.com/mancinimassimiliano/adagraph

• Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_Unsupervised_Visual_Domain_Adaptation_A_Deep_Max-Margin_Gaussian_Process_Approach_CVPR_2019_paper.pdf  [[Project]] https://seqam-lab.github.io/GPDA/

• Contrastive Adaptation Network for Unsupervised Domain Adaptation [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Kang_Contrastive_Adaptation_Network_for_Unsupervised_Domain_Adaptation_CVPR_2019_paper.pdf

• Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Liang_Distant_Supervised_Centroid_Shift_A_Simple_and_Efficient_Approach_to_CVPR_2019_paper.pdf

• Unsupervised Domain Adaptation via Calibrating Uncertainties [[CVPRW2019]] http://openaccess.thecvf.com/content_CVPRW_2019/papers/Uncertainty and Robustness in Deep Visual Learning/Han_Unsupervised_Domain_Adaptation_via_Calibrating_Uncertainties_CVPRW_2019_paper.pdf

• Bayesian Uncertainty Matching for Unsupervised Domain Adaptation [[IJCAI2019]] https://arxiv.org/abs/1906.09693v1

• Unsupervised Domain Adaptation for Distance Metric Learning [[ICLR2019]] https://openreview.net/forum?id=BklhAj09K7

• Co-regularized Alignment for Unsupervised Domain Adaptation [[NIPS2018]] http://papers.nips.cc/paper/8146-co-regularized-alignment-for-unsupervised-domain-adaptation

• Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation [[TIP 2018]] https://ieeexplore.ieee.org/document/8362753/

• Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhengming_Ding_Graph_Adaptive_Knowledge_ECCV_2018_paper.pdf

• Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation [[CVPR2018]] http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Aligning_Infinite-Dimensional_Covariance_CVPR_2018_paper.pdf

• Unsupervised Domain Adaptation with Distribution Matching Machines [[AAAI2018]] http://ise.thss.tsinghua.edu.cn/~mlong/doc/distribution-matching-machines-aaai18.pdf

• Learning to cluster in order to transfer across domains and tasks [[ICLR2018]] https://openreview.net/forum?id=ByRWCqvT-  [[Bolg]] https://mlatgt.blog/2018/04/29/learning-to-cluster/  [[Pytorch]] https://github.com/GT-RIPL/L2C

• Self-Ensembling for Visual Domain Adaptation [[ICLR2018]] https://openreview.net/forum?id=rkpoTaxA-

• Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation [[ICLR2018]] https://openreview.net/forum?id=rJWechg0Z  [[TensorFlow]] https://github.com/pmorerio/minimal-entropy-correlation-alignment

• Associative Domain Adaptation [[ICCV2017]] http://openaccess.thecvf.com/content_ICCV_2017/papers/Haeusser_Associative_Domain_Adaptation_ICCV_2017_paper.pdf  [[TensorFlow]] https://github.com/haeusser/learning_by_association  [[Pytorch]] https://github.com/corenel/pytorch-atda

• AutoDIAL: Automatic DomaIn Alignment Layers [[ICCV2017]] http://openaccess.thecvf.com/content_ICCV_2017/papers/Carlucci_AutoDIAL_Automatic_DomaIn_ICCV_2017_paper.pdf

• Asymmetric Tri-training for Unsupervised Domain Adaptation [[ICML2017]] http://proceedings.mlr.press/v70/saito17a.html  [[TensorFlow]] https://github.com/ksaito-ut/atda

• Learning Transferrable Representations for Unsupervised Domain Adaptation [[NIPS2016]] http://papers.nips.cc/paper/6360-learning-transferrable-representations-for-unsupervised-domain-adaptation

Journal

• Adaptive Batch Normalization for practical domain adaptation [[Pattern Recognition 2018 ]] https://www.sciencedirect.com/science/article/pii/S003132031830092X

• Unsupervised Domain Adaptation by Mapped Correlation Alignment [[IEEE ACCESS]] https://ieeexplore.ieee.org/abstract/document/8434290/

Semi-supervised DA

• Semi-supervised Domain Adaptation via Minimax Entropy [[ICCV2019]] https://arxiv.org/abs/1904.06487v2  [[Pytorch]] https://github.com/VisionLearningGroup/SSDA_MME

Weakly-Supervised DA

• Butterfly: Robust One-step Approach towards Wildly-unsupervised Domain Adaptation [[arXiv on 19 May 2019]] https://arxiv.org/abs/1905.07720v1

• Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration [[CVPR2019]] https://arxiv.org/abs/1904.13179

• Transferable Curriculum for Weakly-Supervised Domain Adaptation [[AAAI2019]] http://ise.thss.tsinghua.edu.cn/~mlong/doc/transferable-curriculum-aaai19.pdf

Zero-shot DA

• Zero-shot Domain Adaptation Based on Attribute Information [[arXiv 13 Mar 2019]] https://arxiv.org/abs/1903.05312v1

• Generalized Zero-Shot Learning with Deep Calibration Network [NIPS2018] http://ise.thss.tsinghua.edu.cn/~mlong/doc/deep-calibration-network-nips18.pdf

• Zero-Shot Deep Domain Adaptation [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Kuan-Chuan_Peng_Zero-Shot_Deep_Domain_ECCV_2018_paper.pdf

One-shot DA

• One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning [[arxiv]] https://arxiv.org/abs/1802.01557

• One-Shot Adaptation of Supervised Deep Convolutional Models [[ICLR Workshop 2014]] https://arxiv.org/abs/1312.6204

Few-shot DA

• d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding [[CVPR2019 Oral]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_d-SNE_Domain_Adaptation_Using_Stochastic_Neighborhood_Embedding_CVPR_2019_paper.pdf

• Few-Shot Adversarial Domain Adaptation [[NIPS2017]] http://papers.nips.cc/paper/7244-few-shot-adversarial-domain-adaptation

Image-to-Image Translation

Arxiv

• MISO: Mutual Information Loss with Stochastic Style Representations for Multimodal Image-to-Image Translation [[arXiv 11 Feb 2019]] https://arxiv.org/abs/1902.03938

• TraVeLGAN: Image-to-image Translation by Transformation Vector Learning [[arXiv 25 Feb 2019]] https://arxiv.org/abs/1902.09631

Conference

• Batch Weight for Domain Adaptation With Mass Shift [[ICCV2019]] https://arxiv.org/abs/1905.12760

• Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer [[ICLR2019]] https://openreview.net/forum?id=BylE1205Fm  [[Pytorch]] https://github.com/oripress/ContentDisentanglement

• Unsupervised Attention-guided Image-to-Image Translation [[NIPS2018]] https://papers.nips.cc/paper/7627-unsupervised-attention-guided-image-to-image-translation

• Image-to-image translation for cross-domain disentanglement [[NIPS2018]] https://papers.nips.cc/paper/7404-image-to-image-translation-for-cross-domain-disentanglement

• One-Shot Unsupervised Cross Domain Translation [[NIPS2018]] http://papers.nips.cc/paper/7480-one-shot-unsupervised-cross-domain-translation

• A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation [[NIPS2018]] http://papers.nips.cc/paper/7525-a-unified-feature-disentangler-for-multi-domain-image-translation-and-manipulation

• Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound [[NIPS2018]] http://papers.nips.cc/paper/8236-unsupervised-image-to-image-translation-using-domain-specific-variational-information-bound

• Multi-view Adversarially Learned Inference for Cross-domain Joint Distribution Matching [[KDD2018]] http://www.kdd.org/kdd2018/accepted-papers/view/multi-view-adversarially-learned-inference-for-cross-domain-joint-distribut

• Unpaired Multi-Domain Image Generation via Regularized Conditional GANs [[IJCAI2018]] https://www.ijcai.org/proceedings/2018/0354.pdf  [[TensorFlow]] https://github.com/xudonmao/RegCGAN

• Improving Shape Deformation in Unsupervised Image-to-Image Translation [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Aaron_Gokaslan_Improving_Shape_Deformation_ECCV_2018_paper.pdf

• NAM: Non-Adversarial Unsupervised Domain Mapping [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Yedid_Hoshen_Separable_Cross-Domain_Translation_ECCV_2018_paper.pdf

• AugGAN: Cross Domain Adaptation with GAN-based Data Augmentation [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Sheng-Wei_Huang_AugGAN_Cross_Domain_ECCV_2018_paper.pdf

• Recycle-GAN: Unsupervised Video Retargeting [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Aayush_Bansal_Recycle-GAN_Unsupervised_Video_ECCV_2018_paper.pdf  [[Project]] http://www.cs.cmu.edu/~aayushb/Recycle-GAN/

• Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Minjun_Li_Unsupervised_Image-to-Image_Translation_ECCV_2018_paper.pdf

• Diverse Image-to-Image Translation via Disentangled Representations [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Hsin-Ying_Lee_Diverse_Image-to-Image_Translation_ECCV_2018_paper.pdf  [[Pytorch Official ]] https://github.com/HsinYingLee/DRIT/  [[Tensorflow]] https://github.com/taki0112/DRIT-Tensorflow

• Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Chao_Wang_Discriminative_Region_Proposal_ECCV_2018_paper.pdf

• Multimodal Unsupervised Image-to-Image Translation [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Xun_Huang_Multimodal_Unsupervised_Image-to-image_ECCV_2018_paper.pdf  [[Pytorch Official ]] https://github.com/nvlabs/MUNIT

• JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets [[ICML2018]] http://proceedings.mlr.press/v80/pu18a.html  [[TensorFlow Official ]] https://github.com/sdai654416/Joint-GAN

• DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks [[CVPR2018]] http://openaccess.thecvf.com/content_cvpr_2018/papers/Ma_DA-GAN_Instance-Level_Image_CVPR_2018_paper.pdf

• StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation [[CVPR2018]] https://arxiv.org/abs/1711.09020  [[Pytorch Official ]] https://github.com/yunjey/StarGAN

• Conditional Image-to-Image Translation [[CVPR2018]] https://arxiv.org/abs/1805.00251v1

• Toward Multimodal Image-to-Image Translation [[NIPS2017]] https://arxiv.org/abs/1711.11586  [[Project]] https://junyanz.github.io/BicycleGAN/  [[Pyotorch Official ]] https://github.com/junyanz/BicycleGAN

• Unsupervised Image-to-Image Translation Networks [[NIPS2017]] http://papers.nips.cc/paper/6672-unsupervised-image-to-image-translation-networks  [[Pytorch Official ]] https://github.com/mingyuliutw/unit

• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks [[ICCV2017 extended version ]] https://arxiv.org/abs/1703.10593v4  [[Pytorch Official ]] https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

• Image-to-Image Translation with Conditional Adversarial Nets [[CVPR2017]] https://arxiv.org/abs/1611.07004  [[Project]] https://phillipi.github.io/pix2pix/  [[Pytorch Official ]] https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

• Learning to Discover Cross-Domain Relations with Generative Adversarial Networks [[ICML2017]] https://arxiv.org/abs/1703.05192  [[Pytorch Official ]] https://github.com/SKTBrain/DiscoGAN

• Unsupervised Cross-Domain Image Generation [[ICLR2017 Poster]] https://openreview.net/forum?id=Sk2Im59ex  [[TensorFlow]] https://github.com/yunjey/domain-transfer-network

• Coupled Generative Adversarial Networks [[NIPS2016]] http://papers.nips.cc/paper/6544-coupled-generative-adversarial-networks  [[Pytorch Official ]] https://github.com/mingyuliutw/cogan

Disentangled Representation Learning

Arxiv

• Towards a Definition of Disentangled Representations [[arXiv 5 Dec 2018]] https://arxiv.org/abs/1812.02230

Conference

• Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer [[ICLR2019]] https://openreview.net/forum?id=BylE1205Fm  [[Pytorch]] https://github.com/oripress/ContentDisentanglement

• Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies [[NIPS2018]] https://papers.nips.cc/paper/8193-life-long-disentangled-representation-learning-with-cross-domain-latent-homologies

• Image-to-image translation for cross-domain disentanglement [[NIPS2018]] https://papers.nips.cc/paper/7404-image-to-image-translation-for-cross-domain-disentanglement

Open Set DA

Arxiv

• Known-class Aware Self-ensemble for Open Set Domain Adaptation [[arXiv 3 May 2019]] https://arxiv.org/abs/1905.01068v1

Conference

• Separate to Adapt: Open Set Domain Adaptation via Progressive Separation [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Separate_to_Adapt_Open_Set_Domain_Adaptation_via_Progressive_Separation_CVPR_2019_paper.pdf

• Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Tan_Weakly_Supervised_Open-Set_Domain_Adaptation_by_Dual-Domain_Collaboration_CVPR_2019_paper.pdf

• Learning Factorized Representations for Open-set Domain Adaptation [[ICLR2019]] https://openreview.net/pdf?id=SJe3HiC5KX

• Open Set Domain Adaptation by Backpropagation [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Kuniaki_Saito_Adversarial_Open_Set_ECCV_2018_paper.pdf  [[Pytorch Official ]] https://github.com/ksaito-ut/OPDA_BP  [[Tensorflow]] https://github.com/Mid-Push/Open_set_domain_adaptation  [[Pytorch]] https://github.com/YU1ut/openset-DA

• Open Set Domain Adaptation [[ICCV2017]] http://openaccess.thecvf.com/content_ICCV_2017/papers/Busto_Open_Set_Domain_ICCV_2017_paper.pdf

Partial DA

Arxiv

• Tackling Partial Domain Adaptation with Self-Supervision [[arXiv 12 Jun 2019]] https://arxiv.org/abs/1906.05199v1

• Selective Transfer with Reinforced Transfer Network for Partial Domain Adaptation [[arXiv 26 May 2019]] https://arxiv.org/abs/1905.10756v1

• Domain Adversarial Reinforcement Learning for Partial Domain Adaptation [[arXiv 10 May 2019]] https://arxiv.org/abs/1905.04094v1

Conference

• Learning to Transfer Examples for Partial Domain Adaptation [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Cao_Learning_to_Transfer_Examples_for_Partial_Domain_Adaptation_CVPR_2019_paper.pdf

• Partial Adversarial Domain Adaptation [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhangjie_Cao_Partial_Adversarial_Domain_ECCV_2018_paper.pdf  [[Pytorch Official ]] https://github.com/thuml/PADA

• Importance Weighted Adversarial Nets for Partial Domain Adaptation [[CVPR2018]] http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Importance_Weighted_Adversarial_CVPR_2018_paper.html

• Partial Transfer Learning with Selective Adversarial Networks [[CVPR2018]] http://openaccess.thecvf.com/content_cvpr_2018/papers/Cao_Partial_Transfer_Learning_CVPR_2018_paper.pdf [[paper weekly]] http://www.paperweekly.site/papers/1388  [[Pytorch Official  & Caffe official ]] https://github.com/thuml/SAN

Multi Source DA

Conference

• Moment Matching for Multi-Source Domain Adaptation [[ICCV2019]] https://arxiv.org/abs/1812.01754v4  [[Pytorch]] http://ai.bu.edu/M3SDA/

• Multi-Domain Adversarial Learning [[ICLR2019]] https://openreview.net/forum?id=Sklv5iRqYX

• Algorithms and Theory for Multiple-Source Adaptation [[NIPS2018]] https://papers.nips.cc/paper/8046-algorithms-and-theory-for-multiple-source-adaptation

• Adversarial Multiple Source Domain Adaptation [[NIPS2018]] http://papers.nips.cc/paper/8075-adversarial-multiple-source-domain-adaptation  [[Pytorch]] https://github.com/KeiraZhao/MDAN

• Boosting Domain Adaptation by Discovering Latent Domains [[CVPR2018]] http://openaccess.thecvf.com/content_cvpr_2018/papers/Mancini_Boosting_Domain_Adaptation_CVPR_2018_paper.pdf

• Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift [[CVPR2018]] https://arxiv.org/abs/1803.00830  [[Pytorch]] https://github.com/HCPLab-SYSU/MSDA

Journal

• A survey of multi-source domain adaptation [[Information Fusion]] https://www.sciencedirect.com/science/article/pii/S1566253514001316

Multi Target DA

• Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach [[arXiv]] https://arxiv.org/abs/1810.11547v1

Multi Step DA

Arxiv

• Adversarial Domain Adaptation for Stance Detection [[arXiv]] https://arxiv.org/abs/1902.02401

• Ensemble Adversarial Training: Attacks and Defenses [[arXiv]] https://arxiv.org/abs/1705.07204

Conference

• Distant domain transfer learning [[AAAI2017]] http://www.ntu.edu.sg/home/sinnopan/publications/[AAAI17]Distant Domain Transfer Learning.pdf

Heterogeneous DA

• Heterogeneous Domain Adaptation via Soft Transfer Network [[ACM MM2019]] https://arxiv.org/abs/1908.10552v1

Target-agnostic DA

• Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Blending-Target_Domain_Adaptation_by_Adversarial_Meta-Adaptation_Networks_CVPR_2019_paper.pdf

Source-agnostic DA

• Domain Agnostic Learning with Disentangled Representations [[ICML2019]] http://proceedings.mlr.press/v97/peng19b/peng19b.pdf  [[Pytorch]] https://github.com/VisionLearningGroup/DAL

Model Selection

• Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation [[ICML2019]] http://proceedings.mlr.press/v97/you19a/you19a.pdf  [[Pytorch]] https://github.com/thuml/Deep-Embedded-Validation

General Transfer Learning

Domain Generalization

Arxiv

• Towards Shape Biased Unsupervised Representation Learning for Domain Generalization [[18 Sep 2019]] https://arxiv.org/abs/1909.08245v1

• A Generalization Error Bound for Multi-class Domain Generalization [[24 May 2019]] https://arxiv.org/abs/1905.10392v1

• Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization [[29 Apr 2019]] https://arxiv.org/abs/1904.12543v1

• Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models [[9 Dec 2018]] https://arxiv.org/abs/1812.03407v1

Conference

• Episodic Training for Domain Generalization [[ICCV2019 Oral]] https://arxiv.org/abs/1902.00113  [[code]] https://github.com/HAHA-DL/Episodic-DG

• Feature-Critic Networks for Heterogeneous Domain Generalization [[ICML2019]] http://proceedings.mlr.press/v97/li19l/li19l.pdf  [[Pytorch]] https://github.com/liyiying/Feature_Critic

• Domain Generalization by Solving Jigsaw Puzzles [[CVPR2019 Oral]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Carlucci_Domain_Generalization_by_Solving_Jigsaw_Puzzles_CVPR_2019_paper.pdf  [[Pytorch]] https://github.com/fmcarlucci/JigenDG

• MetaReg: Towards Domain Generalization using Meta-Regularization [[NIPS2018]] https://papers.nips.cc/paper/7378-metareg-towards-domain-generalization-using-meta-regularization

• Deep Domain Generalization via Conditional Invariant Adversarial Networks [[ECCV2018]] http://openaccess.thecvf.com/content_ECCV_2018/papers/Ya_Li_Deep_Domain_Generalization_ECCV_2018_paper.pdf

• Domain Generalization with Adversarial Feature Learning [[CVPR2018]] http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Domain_Generalization_With_CVPR_2018_paper.pdf

Domain Randomization

• DeceptionNet: Network-Driven Domain Randomization [[arXiv 4 Apr 2019]] https://arxiv.org/abs/1904.02750

Meta-Learning

Unsupervised Learning via Meta-Learning [[arXiv]] https://arxiv.org/abs/1810.02334

Transfer Metric Learning

• Transfer Metric Learning: Algorithms, Applications and Outlooks [[arXiv]] https://arxiv.org/abs/1810.03944

Others

Arxiv

• When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets [[arXiv 13 Dec 2018]] https://arxiv.org/abs/1812.05313

Conference

• Domain Agnostic Learning with Disentangled Representations [[ICML2019]] https://arxiv.org/abs/1904.12347v1

• Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization [[CVPR2019]] https://arxiv.org/abs/1904.08631  [[Pytorch]] https://github.com/junbaoZHUO/UODTN

Applications

Object Detection

Arxiv

Conference

• Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night [[IJCNN2019 Oral]] https://ieeexplore.ieee.org/document/8852008  [[Project]] https://github.com/viniciusarruda/cross-domain-car-detection

• Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection [[ICCV2019 Oral]] https://arxiv.org/abs/1909.00597v1

• A Robust Learning Approach to Domain Adaptive Object Detection [[ICCV2019]] https://arxiv.org/abs/1904.02361  [[code]] https://github.com/mkhodabandeh/robust_domain_adaptation

• Multi-adversarial Faster-RCNN for Unrestricted Object Detection [[ICCV2019]] https://arxiv.org/abs/1907.10343

• Exploring Object Relation in Mean Teacher for Cross-Domain Detection [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Cai_Exploring_Object_Relation_in_Mean_Teacher_for_Cross-Domain_Detection_CVPR_2019_paper.pdf

• Adapting Object Detectors via Selective Cross-Domain Alignment [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_Adapting_Object_Detectors_via_Selective_Cross-Domain_Alignment_CVPR_2019_paper.pdf

• Automatic adaptation of object detectors to new domains using self-training [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/RoyChowdhury_Automatic_Adaptation_of_Object_Detectors_to_New_Domains_Using_Self-Training_CVPR_2019_paper.pdf  [[Project]] http://vis-www.cs.umass.edu/unsupVideo/

• Towards Universal Object Detection by Domain Attention [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Towards_Universal_Object_Detection_by_Domain_Attention_CVPR_2019_paper.pdf

• Strong-Weak Distribution Alignment for Adaptive Object Detection [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Saito_Strong-Weak_Distribution_Alignment_for_Adaptive_Object_Detection_CVPR_2019_paper.pdf  [[Pytorch]] https://github.com/VisionLearningGroup/DA_Detection

• Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_Diversify_and_Match_A_Domain_Adaptive_Representation_Learning_Paradigm_for_CVPR_2019_paper.pdf

• Cross-Domain Weakly-Supervised Object Detection Through Progressive Domain Adaptation [[CVPR2018]] https://arxiv.org/abs/1803.11365

• Domain Adaptive Faster R-CNN for Object Detection in the Wild [[CVPR2018]] http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Domain_Adaptive_Faster_CVPR_2018_paper.pdf  [[Caffe2]] https://github.com/krumo/Detectron-DA-Faster-RCNN  [[Caffe]] https://github.com/yuhuayc/da-faster-rcnn  [[Pytorch under developing ]] https://github.com/zhaoxin94/awsome-domain-adaptation/blob/master

Semantic Segmentation

Arxiv

• Restyling Data: Application to Unsupervised Domain Adaptation [[24 Sep 2019]] https://arxiv.org/abs/1909.10900

• Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation [[arXiv 2 Sep 2019]] https://arxiv.org/abs/1909.00781v1

• Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach [[arXiv 26 Aug 2019]] https://arxiv.org/abs/1908.09547

Conference

• MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent Labeling [[WACV2020]] https://arxiv.org/abs/1909.13776

• Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation [[ICCV2019]] https://arxiv.org/abs/1901.05946

• Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation [[ICCV2019]] https://arxiv.org/abs/1904.00876v1

• Domain Adaptation for Semantic Segmentation with Maximum Squares Loss [[ICCV2019]] https://arxiv.org/abs/1909.13589  [[Pytorch]] https://github.com/ZJULearning/MaxSquareLoss

• Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation [[ICCV2019]] https://arxiv.org/abs/1909.00589v1

• DADA: Depth-aware Domain Adaptation in Semantic Segmentation [[ICCV2019]] https://arxiv.org/abs/1904.01886  [[code]] https://github.com/valeoai/DADA

• Domain Adaptation for Structured Output via Discriminative Patch Representations [[ICCV2019 Oral]] https://arxiv.org/abs/1901.05427  [[Project]] https://sites.google.com/site/yihsuantsai/research/iccv19-adapt-seg

• Not All Areas Are Equal: Transfer Learning for Semantic Segmentation via Hierarchical Region Selection [[CVPR2019 Oral  PDF Coming Soon ]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Sun_Not_All_Areas_Are_Equal_Transfer_Learning_for_Semantic_Segmentation_CVPR_2019_paper.pdf

• CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_CrDoCo_Pixel-Level_Domain_Transfer_With_Cross-Domain_Consistency_CVPR_2019_paper.pdf  [[Project]] https://yunchunchen.github.io/CrDoCo/

• Bidirectional Learning for Domain Adaptation of Semantic Segmentation [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Bidirectional_Learning_for_Domain_Adaptation_of_Semantic_Segmentation_CVPR_2019_paper.pdf  [[Pytorch]] https://github.com/liyunsheng13/BDL

• Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Learning_Semantic_Segmentation_From_Synthetic_Data_A_Geometrically_Guided_Input-Output_CVPR_2019_paper.pdf

• All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Chang_All_About_Structure_Adapting_Structural_Information_Across_Domains_for_Boosting_CVPR_2019_paper.pdf  [[Pytorch]] https://github.com/a514514772/DISE-Domain-Invariant-Structure-Extraction

• DLOW: Domain Flow for Adaptation and Generalization [[CVPR2019 Oral]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Gong_DLOW_Domain_Flow_for_Adaptation_and_Generalization_CVPR_2019_paper.pdf

• Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation [[CVPR2019 Oral]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Luo_Taking_a_Closer_Look_at_Domain_Shift_Category-Level_Adversaries_for_CVPR_2019_paper.pdf  [[Pytorch]] https://github.com/RoyalVane/CLAN

• ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation [[CVPR2019 Oral]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Vu_ADVENT_Adversarial_Entropy_Minimization_for_Domain_Adaptation_in_Semantic_Segmentation_CVPR_2019_paper.pdf  [[Pytorch]] https://github.com/valeoai/ADVENT

• SPIGAN: Privileged Adversarial Learning from Simulation [[ICLR2019]] https://openreview.net/forum?id=rkxoNnC5FQ

• 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

• 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

• 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

• Learning From Synthetic Data: Addressing Domain Shift for Semantic Segmentation [[CVPR2018]] http://openaccess.thecvf.com/content_cvpr_2018/papers/Sankaranarayanan_Learning_From_Synthetic_CVPR_2018_paper.pdf

• Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes [[ICCV2017]] http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhang_Curriculum_Domain_Adaptation_ICCV_2017_paper.pdf  [[Journal Version]] https://arxiv.org/abs/1812.09953v3

Journal

• Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes [[TIP]] https://arxiv.org/abs/1904.09092v1

Person Re-identification

Arxiv

• Domain Adaptive Attention Model for Unsupervised Cross-Domain Person Re-Identification [[arXiv 25 May 2019]] https://arxiv.org/abs/1905.10529

• Camera Adversarial Transfer for Unsupervised Person Re-Identification [[arXiv 2 Apr 2019]] https://arxiv.org/abs/1904.01308

• EANet: Enhancing Alignment for Cross-Domain Person Re-identification [[arXiv 29 Dec 2018]] https://arxiv.org/abs/1812.11369  [[Pytorch]] https://github.com/huanghoujing/EANet

• One Shot Domain Adaptation for Person Re-Identification [[arXiv 26 Nov 2018]] https://arxiv.org/abs/1811.10144v1

• Similarity-preserving Image-image Domain Adaptation for Person Re-identification [[arXiv 26 Nov 2018]] https://arxiv.org/abs/1811.10551v1

Conference

• Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification [[ICCV2019 Oral]] https://arxiv.org/abs/1811.10144  [[Pytorch]] https://github.com/OasisYang/SSG

• 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

• Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification [[CVPR2018]] https://arxiv.org/abs/1711.07027v3

Video Domain Adaptation

Arxiv

• Image to Video Domain Adaptation Using Web Supervision [[5 Aug 2019]] https://arxiv.org/abs/1908.01449

Conference

• Temporal Attentive Alignment for Large-Scale Video Domain Adaptation [[ICCV2019 Oral]] https://arxiv.org/abs/1907.12743  [[Pytorch]] https://github.com/olivesgatech/TA3N

• Temporal Attentive Alignment for Video Domain Adaptation [[CVPRW 2019]] https://arxiv.org/abs/1905.10861v5  [[Pytorch]] https://github.com/olivesgatech/TA3N

Medical Related

• Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation [[arXiv 29 Aug 2019]] https://arxiv.org/abs/1907.13590

• Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation [[arXiv on 24 Jan 2019]] https://arxiv.org/abs/1901.08211v1

• Unsupervised domain adaptation for medical imaging segmentation with self-ensembling [[arXiv 14 Nov 2018]] https://arxiv.org/abs/1811.06042v1

Monocular Depth Estimation

• Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation [[CVPR2019]] http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Geometry-Aware_Symmetric_Domain_Adaptation_for_Monocular_Depth_Estimation_CVPR_2019_paper.pdf

• Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer [[CVPR2018]] http://breckon.eu/toby/publications/papers/abarghouei18monocular.pdf

Others

Arxiv

• DANE: Domain Adaptive Network Embedding [[arXiv 3 Jun 2019]] https://arxiv.org/abs/1906.00684v1

• Active Adversarial Domain Adaptation [[arXiv 16 Apr 2019]] https://arxiv.org/abs/1904.07848v1

Conference

• GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition [[ICCV2019]] https://arxiv.org/abs/1907.09653

• Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning [[IJCNN]] https://arxiv.org/abs/1904.02654

• Adversarial Adaptation of Scene Graph Models for Understanding Civic Issues [[WWW2019]] https://arxiv.org/abs/1901.10124

Benchmarks

• Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation [[arXiv 26 Jun]] https://arxiv.org/abs/1806.09755v1  [[Project]] http://ai.bu.edu/syn2real/

Library

• [Xlearn:Transfer Learning Library] https://github.com/thuml/Xlearn

• [deep-transfer-learning:a PyTorch library for deep transfer learning] https://github.com/easezyc/deep-transfer-learning

• [salad:a Semi-supervised Adaptive Learning Across Domains] https://domainadaptation.org/

Other Resources

• [transferlearning] https://github.com/jindongwang/transferlearning

原文链接：

https://github.com/zhaoxin94/awsome-domain-adaptation

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