领域自适应学习论文大列表

【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。 本文整理了近几年期刊会议上领域自适应学习相关的论文与代码

会议论文

•  Dhouib's: Revisiting epsilon, gamma, tau similarity learning for domain adaptation[NeurIPS2018](http://papers.nips.cc/paper/7969revisitingepsilongammatausimilaritylearningfordomainadaptation.pdf)

•  CDAN: Conditional Adversarial Domain Adaptation[[NeurIPS2018]](http://papers.nips.cc/paper/7436conditionaladversarialdomainadaptation.pdf)

•  Magliacane's: Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions[[NeurIPS2018]](http://papers.nips.cc/paper/8282domainadaptationbyusingcausalinferencetopredictinvariantconditionaldistributions.pdf)

•  CoDA: Coregularized Alignment for Unsupervised Domain Adaptation[[NeurIPS2018]](http://papers.nips.cc/paper/8146coregularizedalignmentforunsuperviseddomainadaptation.pdf)

•  JDDA：Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation[[arXiv 3 Nov 2018]](https://arxiv.org/pdf/1808.09347.pdf)

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

•  GAKT: Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/html/Zhengming_Ding_Graph_Adaptive_Knowledge_ECCV_2018_paper.html)

•  Kang's: Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: the Benefit of Target Expectation Maximization [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/html/Guoliang_Kang_Deep_Adversarial_Attention_ECCV_2018_paper.html)

•  MEDA: Visual Domain Adaptation with Manifold Embedded Distribution Alignment [[ACM MM2018]](https://arxiv.org/abs/1807.07258) [[Matlab(Official)]](https://github.com/jindongwang/transferlearning/tree/master/code/traditional/MEDA#medamanifoldembeddeddistributionalignment)

•  CyCADA: Cycle Consistent Adversarial Domain Adaptation [[ICML2018]](http://proceedings.mlr.press/v80/hoffman18a/hoffman18a.pdf) [[Pytorch(Official)]](https://github.com/jhoffman/cycada_release)

•  MSTN: Learning Semantic Representations for Unsupervised Domain Adaptation [[ICML2018]](http://proceedings.mlr.press/v80/xie18c/xie18c.pdf) [[Tensorflow(Official)]](https://github.com/MidPush/MovingSemanticTransferNetwork)

•  DeppJDOT: DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation [[arXiv 27 Mar 2018]](https://arxiv.org/pdf/1803.10081.pdf) [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/html/Bharath_Bhushan_Damodaran_DeepJDOT_Deep_Joint_ECCV_2018_paper.html)

•  Saito's: Open Set Domain Adaptation by Backpropagation [[arXiv 27 Apr 2018]](https://arxiv.org/abs/1804.10427) [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/html/Kuniaki_Saito_Adversarial_Open_Set_ECCV_2018_paper.html) [[TensorFlow]](https://github.com/MidPush/Open_set_domain_adaptation) [[Pytorch]](https://github.com/YU1ut/opensetDA)

•  CPUA: Simple Domain Adaptation with Class Prediction Uncertainty Alignment [[ICML2018 Poster]](https://arxiv.org/abs/1804.04448)

•  PDA: Importance Weighted Adversarial Nets for Partial Domain Adaptation [[CVPR2018]](https://arxiv.org/abs/1803.09210)

•  MCD_DA: Maximum Classifier Discrepancy for Unsupervised Domain Adaptation [[CVPR2018]](https://arxiv.org/abs/1712.02560) [[Pytorch(Official)]](https://github.com/miltokyo/MCD_DA)

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

•  DIFA: Adversarial Feature Augmentation for Unsupervised Domain Adaptation [[CVPR2018]](https://arxiv.org/abs/1711.08561) [[TensorFlow 1.3(Official)]](https://github.com/ricvolpi/adversarialfeatureaugmentation)

•  SAN: Partial Transfer Learning with Selective Adversarial Networks [[CVPR2018]](https://arxiv.org/abs/1707.07901)[[paper weekly]](http://www.paperweekly.site/papers/1388)

•  DupGAN: Duplex Generative Adversarial Network for Unsupervised Domain Adaptation [[CVPR2018]](http://vipl.ict.ac.cn/uploadfile/upload/2018041610083083.pdf) [[Pytorch 0.1(Official)]](http://vipl.ict.ac.cn/view_database.php?id=6)

•  GTA: 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)

•  SimNet: Unsupervised Domain Adaptation with Similarity Learning [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/1887.pdf)

•  KWC,KOT: Aligning InfiniteDimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/3383.pdf)

•  DCTN: Deep Cocktail Network: Multisource Unsupervised Domain Adaptation with Category Shift [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/1880.pdf)

•  iCAN: Collaborative and Adversarial Network for Unsupervised Domain Adaptation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/1410.pdf)

•  RAAN: ReWeighted Adversarial Adaptation Network for Unsupervised Domain Adaptation [[CVPR2018]](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/1224.pdf)

•  MADA: MultiAdversarial Domain Adaptation [[AAAI2018]](http://ise.thss.tsinghua.edu.cn/~mlong/doc/multiadversarialdomainadaptationaaai18.pdf) [[Caffe(Official)]](https://github.com/thuml/MADA)

•  WDGRL: Wasserstein Distance Guided Representation Learning for Domain Adaptation [[AAAI2018]](https://arxiv.org/abs/1707.01217) [[Tensorflow 1.3.0(Official)]](https://github.com/RockySJ/WDGRL) [[Pytorch]](https://github.com/jvanvugt/pytorchdomainadaptation)

•  DIRTT: A DIRTT Approach to Unsupervised Domain Adaptation [[ICLR2018]](https://openreview.net/forum?id=H1qTMAW) [[Tensorflow(Official)]](https://github.com/RuiShu/dirtt)

•  MT: Selfensembling for Visual Domain Adaptation [[ICLR2018]](https://openreview.net/pdf?id=rkpoTaxA) [[Pytorch(Official)]](https://github.com/Britefury/selfensemblevisualdomainadapt/)

•  CCN: Learning to Cluster in Order to Transfer Across Domains and Tasks [[ICLR2018]](https://openreview.net/pdf?id=ByRWCqvT)

•  MECA: MinimalEntropy Correlation Alignment for Unsupervised Deep Domain Adaptation [[ICLR2018]](https://openreview.net/forum?id=rJWechg0Z)[[Tensorflow(Official)]](https://github.com/pmorerio/minimalentropycorrelationalignment)

•  ATI: Open Set Domain Adaptation [[ICCV2017]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Busto_Open_Set_Domain_ICCV_2017_paper.pdf) [[Matlab(Official)]](https://github.com/Heliot7/opensetda)

•  AutoDIAL: Automatic DomaIn Alignment Layers [[ICCV2017]](https://www.computer.org/csdl/proceedings/iccv/2017/1032/00/1032f077.pdf) [[Caffe(Official)]](https://github.com/ducksoup/autodial)

•  DAassoc: Associative Domain Adaptation [[ICCV2017]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Haeusser_Associative_Domain_Adaptation_ICCV_2017_paper.pdf)[[Tensorflow(Official)]](https://github.com/haeusser/learning_by_association)

•  TAISL: When Unsupervised Domain Adaptation Meets Tensor Representations [[ICCV2017]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Lu_When_Unsupervised_Domain_ICCV_2017_paper.pdf) [[Matlab(Official)]](https://github.com/poppinace/TAISL)

•  CCSA: Unified Deep Supervised Domain Adaptation and Generalization [[ICCV2017]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Motiian_Unified_Deep_Supervised_ICCV_2017_paper.pdf) [[Keras(Official)]](https://github.com/samotiian/CCSA)

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

•  JDOT: Joint Distribution Optimal Transportation for Domain Adaptation [[NIPS2017]](http://papers.nips.cc/paper/6963jointdistributionoptimaltransportationfordomainadaptation.pdf) [[Python(Official)]](https://github.com/rflamary/JDOT)

•  FADA: FewShot Adversarial Domain Adaptation [[NIPS2017]](https://papers.nips.cc/paper/7244fewshotadversarialdomainadaptation.pdf)

•  ADDA: 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/pytorchadda) [[Pytorch]](https://github.com/jvanvugt/pytorchdomainadaptation)

•  PixelDA: Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks [[CVPR2017]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Bousmalis_Unsupervised_PixelLevel_Domain_CVPR_2017_paper.pdf)[[Tensorflow(Official)]](https://github.com/tensorflow/models/tree/master/research/domain_adaptation) [[Pytorch]](https://github.com/vaibhavnaagar/pixelDA_GAN)

•  JGSA: Joint Geometrical and Statistical Alignment for Visual Domain Adaptation [[CVPR2017]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Joint_Geometrical_and_CVPR_2017_paper) [[Matlab(Official)]](https://www.uow.edu.au/~jz960/)

•  ILS: Learning an Invariant Hilbert Space for Domain Adaptation [[CVPR2017]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Herath_Learning_an_Invariant_CVPR_2017_paper.pdf) [[Matlab(Official)]](https://bitbucket.org/sherath/ils/src)

•  DAH: Deep Hashing Network for Unsupervised Domain Adaptation [[CVPR2017]](http://openaccess.thecvf.com/content_cvpr_2017/supplemental/Venkateswara_Deep_Hashing_Network_2017_CVPR_supplemental.pdf) [[Matlab(Official)]](https://github.com/hemanthdv/dahash)

•  Wu's: A Compact DNN: Approaching GoogLeNetLevel Accuracy of Classification and Domain Adaptation [[CVPR2017]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Wu_A_Compact_DNN_CVPR_2017_paper.pdf)

•  WDAN: Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation [[CVPR2017]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yan_Mind_the_Class_CVPR_2017_paper.pdf) [[Caffe(Official)]](https://github.com/yhldhit/WMMDCaffe)

•  JAN: Deep Transfer Learning with Joint Adaptation Networks [[ICML2017]](https://github.com/barebell/DA/blob/master/proceedings.mlr.press/v70/long17a/long17a.pdf) [[Pytorch 0.2.0_3(Official)]](https://github.com/thuml/Xlearn)

•  ATDA: Asymmetric Tritraining for Unsupervised Domain Adaptation [[ICML2017]](https://github.com/barebell/DA/blob/master/proceedings.mlr.press/v70/saito17a/saito17a.pdf) [[Tensorflow(Official)]](https://github.com/ksaitout/atda) [[Tensorflow]](https://github.com/vtddggg/ATDA)[[Pytorch]](https://github.com/corenel/pytorchatda)

•  AdaBN: Revisiting Batch Normalization For Practical Domain Adaptation [[ICLR2017]](https://openreview.net/pdf?id=BJuysoFeg) [[PR2018]](http://winsty.net/papers/adabn.pdf)

•  DSN: Domain Separation Networks [[NIPS2016]](http://papers.nips.cc/paper/6254domainseparationnetworks) [[Tensorflow(Official)]](https://github.com/tensorflow/models/tree/master/research/domain_adaptation/domain_separation) [[Pytorch]](https://github.com/fungtion/DSN)

•  Sener's: Learning Transferrable Representations for Unsupervised Domain Adaptation [[NIPS2016]](http://papers.nips.cc/paper/6360learningtransferrablerepresentationsforunsuperviseddomainadaptation)

•  RTN: Unsupervised Domain Adaptation with Residual Transfer Networks [[NIPS2016]](https://papers.nips.cc/paper/6110unsuperviseddomainadaptationwithresidualtransfernetworks.pdf)

•  DRCN: Deep ReconstructionClassification Networks for Unsupervised Domain Adaptation [[ECCV2016]](https://arxiv.org/abs/1607.03516) [[Tensorflow 1.0.1(Official)]](https://github.com/ghif/drcn) [[Pytorch]](https://github.com/fungtion/DRCN)

•  Deep CORAL: Deep CORAL: Correlation Alignment for Deep Domain Adaptation [[ECCV2016]](https://arxiv.org/pdf/1607.01719.pdf) [[C(Official)]](https://github.com/VisionLearningGroup/CORAL) [[Pytorch 0.2]](https://github.com/SSARCandy/DeepCORAL)

•  RevGrad: 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/jvanvugt/pytorchdomainadaptation)

期刊论文

•  Wang's survey: Deep visual domain adaptation: A survey [[arXiv 25 Apr 2018]](https://arxiv.org/abs/1802.03601) [[NeuroCompu]](https://www.sciencedirect.com/science/article/pii/S0925231218306684/pdfft?md5=011fcc27c88a40c9e7c88918ba8cc1b2&pid=1s2.0S0925231218306684main.pdf)

•  GsDsDL: Learning Domainshared Groupsparse Representation for Unsupervised Domain Adaptation [PR2018](https://www.sciencedirect.com/sdfe/pdf/download/read/noindex/pii/S0031320318301614/1s2.0S0031320318301614main.pdf)

•  AdaBN: Revisiting Batch Normalization For Practical Domain Adaptation [ICLR2017]](https://openreview.net/pdf?id=BJuysoFeg) [[PR2018](http://winsty.net/papers/adabn.pdf)

•  LDADA: An Embarrassingly Simple Approach to Visual Domain Adaptation [TIP2018](https://ieeexplore.ieee.org/document/8325317/) [Matlab(Official)](https://github.com/poppinace/ldada)

•  DICD: Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation [TIP2018](https://ieeexplore.ieee.org/document/8362753/)

•  HDANA: Heterogeneous Domain Adaptation Network Based on Autoencoder [JPDC2018](https://www.sciencedirect.com/science/article/pii/S0743731517301922)

•  DKTL: Domain Class Consistency Based Transfer Learning For Image Classification Across Domains [InforSci2017](https://www.sciencedirect.com/sdfe/pdf/download/read/noindex/pii/S0020025516313159/1s2.0S0020025516313159main.pdf)

•  Ding's: Deep Domain Generalization With Structured LowRank Constraint [TIP2017](https://github.com/barebell/DA/blob/master/ieeexplore.ieee.org/iel7/83/4358840/08053784.pdf)

•  Venkateswara's survey: DeepLearning Systems for Domain Adaptation in Computer Vision: Learning Transferable Feature Representations [SP Magazine](https://ieeexplore.ieee.org/document/8103149/)

•  BSWDA: Beyond Sharing Weights for Deep Domain Adaptation [TPAMI2016](https://www.computer.org/csdl/trans/tp/preprint/08310033.pdf)

•  SCA: Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization [TPAMI2016](https://www.computer.org/csdl/trans/tp/2017/07/07542175.pdf)

•  DME: DistributionMatching Embedding for Visual Domain Adaptation [JMLR2016](https://github.com/barebell/DA/blob/master/www.jmlr.org/papers/volume17/15207/15207.pdf)

•  DANN: DomainAdversarial Training of Neural Networks [JMLR2016](http://www.jmlr.org/papers/volume17/15239/15239.pdf) [[Tensorflow(Official)]](https://github.com/pumpikano/tfdann) [[Pytorch]](https://github.com/fungtion/DANN) [Pytorch](https://github.com/GRAALResearch/domain_adversarial_neural_network)

•  LSCDA: Unsupervised Domain Adaptation With Label and Structural Consistency [TIP2016](https://ieeexplore.ieee.org/iel7/83/7581012/07569007.pdf)

•  FLDA: FeatureLevel Domain Adaptation [JMLR2016](http://www.jmlr.org/papers/volume17/15206/15206.pdf) [[Matlab(Official)](https://github.com/wmkouw/flda) [Python(Official)](https://github.com/wmkouw/libTLDA)

arXiv 论文

•  GDAN: Causal Generative Domain Adaptation Networks[[arXiv 28 Jun 2018]](https://arxiv.org/pdf/1804.04333.pdf)

•  MADDA: MADDA: Unsupervised Domain Adaptation with Deep Metric Learning [[arXiv 6 Jul 2018]](https://arxiv.org/pdf/1807.02552.pdf) [[Pytorch(Official)]](https://github.com/IssamLaradji/MADDA)

•  FAN: Factorized Adversarial Networks for Unsupervised Domain Adaptation [[arXiv 4 Jun 2018]](https://arxiv.org/pdf/1806.01376.pdf)

•  DiDA: DiDA: Disentangled Synthesis for Domain Adaptation [arXiv 21 Mar 2018](https://arxiv.org/pdf/1805.08019.pdf)

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

•  CMD: Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment [arXiv 28 Mar 2018](https://arxiv.org/pdf/1711.06114.pdf)[Keras(Official)](https://github.com/wzell/mann)

•  CDAAE: CrossDomain Adversarial AutoEncoder[arXiv 17 Apr 2018](https://arxiv.org/pdf/1804.06078.pdf)

•  CPUA: Simple Domain Adaptation with Class Prediction Uncertainty Alignment[arXiv 12 Apr 2018]](https://arxiv.org/pdf/1804.04448.pdf)

•  Tran's: Joint Pixel and Featurelevel Domain Adaptation in the Wild[arXiv 28 Feb 2018](https://arxiv.org/pdf/1803.00068.pdf)

•  InvAuto: Invertible Autoencoder for domain adaptation[arXiv 10 Feb 2018](https://arxiv.org/pdf/1802.06869.pdf)

Github地址：

https://github.com/barebell/DA

END-

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