Detailed resources on unsupervised domain adapation(DA). It includes related papers and the codes. As you see, the list is also not comprehensive. You can pull requests as you will.
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Dhouib's: Revisiting (
$\epsilon$ ,$\gamma$ ,$\tau$ )-similarity learning for domain adaptation[NeurIPS2018] - CDAN: Conditional Adversarial Domain Adaptation[NeurIPS2018]
- Magliacane's: Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions[NeurIPS2018]
- Co-DA: Co-regularized Alignment for Unsupervised Domain Adaptation[NeurIPS2018]
- JDDA：Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation[arXiv 3 Nov 2018]
- PADA: Partial Adversarial Domain Adaptation [ECCV2018] [Pytorch(Official)]
- GAKT: Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation [ECCV2018]
- Kang's: Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: the Benefit of Target Expectation Maximization [ECCV2018]
- MEDA: Visual Domain Adaptation with Manifold Embedded Distribution Alignment [ACM MM2018] [Matlab(Official)]
- CyCADA: Cycle Consistent Adversarial Domain Adaptation [ICML2018] [Pytorch(Official)]
- MSTN: Learning Semantic Representations for Unsupervised Domain Adaptation [ICML2018] [Tensorflow(Official)]
- DeppJDOT: DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation [arXiv 27 Mar 2018] [ECCV2018]
- Saito's: Open Set Domain Adaptation by Backpropagation [arXiv 27 Apr 2018] [ECCV2018] [TensorFlow] [Pytorch]
- CPUA: Simple Domain Adaptation with Class Prediction Uncertainty Alignment [ICML2018 Poster]
- PDA: Importance Weighted Adversarial Nets for Partial Domain Adaptation [CVPR2018]
- MCD_DA: Maximum Classifier Discrepancy for Unsupervised Domain Adaptation [CVPR2018] [Pytorch(Official)]
- RPTDA: Residual Parameter Transfer for Deep Domain Adaptation [CVPR2018]
- DIFA: Adversarial Feature Augmentation for Unsupervised Domain Adaptation [CVPR2018] [TensorFlow 1.3(Official)]
- SAN: Partial Transfer Learning with Selective Adversarial Networks [CVPR2018][paper weekly]
- DupGAN: Duplex Generative Adversarial Network for Unsupervised Domain Adaptation [CVPR2018] [Pytorch 0.1(Official)]
- GTA: Generate To Adapt: Aligning Domains using Generative Adversarial Networks [CVPR2018] [Pytorch(Official)]
- SimNet: Unsupervised Domain Adaptation with Similarity Learning [CVPR2018]
- KWC,KOT: Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation [CVPR2018]
- DCTN: Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift [CVPR2018]
- iCAN: Collaborative and Adversarial Network for Unsupervised Domain Adaptation [CVPR2018]
- RAAN: Re-Weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation [CVPR2018]
- MADA: Multi-Adversarial Domain Adaptation [AAAI2018] [Caffe(Official)]
- WDGRL: Wasserstein Distance Guided Representation Learning for Domain Adaptation [AAAI2018] [Tensorflow 1.3.0(Official)] [Pytorch]
- DIRT-T: A DIRT-T Approach to Unsupervised Domain Adaptation [ICLR2018] [Tensorflow(Official)]
- MT: Self-ensembling for Visual Domain Adaptation [ICLR2018] [Pytorch(Official)]
- CCN: Learning to Cluster in Order to Transfer Across Domains and Tasks [ICLR2018]
- MECA: Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation [ICLR2018] [Tensorflow(Official)]
- ATI: Open Set Domain Adaptation [ICCV2017] [Matlab(Official)]
- AutoDIAL: Automatic DomaIn Alignment Layers [ICCV2017] [Caffe(Official)]
- DAassoc: Associative Domain Adaptation [ICCV2017][Tensorflow(Official)]
- TAISL: When Unsupervised Domain Adaptation Meets Tensor Representations [ICCV2017] [Matlab(Official)]
- CCSA: Unified Deep Supervised Domain Adaptation and Generalization [ICCV2017] [Keras(Official)]
- Luo's: Label Efficient Learning of Transferable Representations acrosss Domains and Tasks [NIPS2017] [Project]
- JDOT: Joint Distribution Optimal Transportation for Domain Adaptation [NIPS2017] [Python(Official)]
- FADA: Few-Shot Adversarial Domain Adaptation [NIPS2017]
- ADDA: Adversarial Discriminative Domain Adaptation [CVPR2017] [Tensorflow(Official)] [Pytorch] [Pytorch]
- PixelDA: Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks [CVPR2017] [Tensorflow(Official)] [Pytorch]
- JGSA: Joint Geometrical and Statistical Alignment for Visual Domain Adaptation [CVPR2017] [Matlab(Official)]
- ILS: Learning an Invariant Hilbert Space for Domain Adaptation [CVPR2017] [Matlab(Official)]
- DAH: Deep Hashing Network for Unsupervised Domain Adaptation [CVPR2017] [Matlab(Official)]
- Wu's: A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation [CVPR2017]
- WDAN: Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation [CVPR2017] [Caffe(Official)]
- JAN: Deep Transfer Learning with Joint Adaptation Networks [ICML2017] [Pytorch 0.2.0_3(Official)]
- ATDA: Asymmetric Tri-training for Unsupervised Domain Adaptation [ICML2017] [Tensorflow(Official)] [Tensorflow] [Pytorch]
- AdaBN: Revisiting Batch Normalization For Practical Domain Adaptation [ICLR2017] [PR2018]
- DSN: Domain Separation Networks [NIPS2016] [Tensorflow(Official)] [Pytorch]
- Sener's: Learning Transferrable Representations for Unsupervised Domain Adaptation [NIPS2016]
- RTN: Unsupervised Domain Adaptation with Residual Transfer Networks [NIPS2016]
- DRCN: Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation [ECCV2016] [Tensorflow 1.0.1(Official)] [Pytorch]
- Deep CORAL: Deep CORAL: Correlation Alignment for Deep Domain Adaptation [ECCV2016] [C(Official)] [Pytorch 0.2]
- RevGrad: Unsupervised Domain Adaptation by Backpropagation [ICML2015] [Caffe(Official)] [Tensorflow] [Pytorch]
- Wang's survey: Deep visual domain adaptation: A survey [arXiv 25 Apr 2018] [NeuroCompu]
- GsDsDL: Learning Domain-shared Group-sparse Representation for Unsupervised Domain Adaptation [PR2018]
- AdaBN: Revisiting Batch Normalization For Practical Domain Adaptation [ICLR2017] [PR2018]
- LDADA: An Embarrassingly Simple Approach to Visual Domain Adaptation [TIP2018] [Matlab(Official)]
- DICD: Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation [TIP2018]
- HDANA: Heterogeneous Domain Adaptation Network Based on Autoencoder [JPDC2018]
- DKTL: Domain Class Consistency Based Transfer Learning For Image Classification Across Domains [InforSci2017]
- Ding's: Deep Domain Generalization With Structured Low-Rank Constraint [TIP2017]
- Venkateswara's survey: Deep-Learning Systems for Domain Adaptation in Computer Vision: Learning Transferable Feature Representations [SP Magazine]
- BSWDA: Beyond Sharing Weights for Deep Domain Adaptation [TPAMI2016]
- SCA: Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization [TPAMI2016]
- DME: Distribution-Matching Embedding for Visual Domain Adaptation [JMLR2016]
- DANN: Domain-Adversarial Training of Neural Networks [JMLR2016] [Tensorflow(Official)] [Pytorch] [Pytorch]
- LSCDA: Unsupervised Domain Adaptation With Label and Structural Consistency [TIP2016]
- FLDA: Feature-Level Domain Adaptation [JMLR2016] [Matlab(Official)] [Python(Official)]
- GDAN: Causal Generative Domain Adaptation Networks[arXiv 28 Jun 2018]
- M-ADDA: M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning [arXiv 6 Jul 2018] [Pytorch(Official)]
- FAN: Factorized Adversarial Networks for Unsupervised Domain Adaptation [arXiv 4 Jun 2018]
- DiDA: DiDA: Disentangled Synthesis for Domain Adaptation [arXiv 21 Mar 2018]
- ARTNs: Unsupervised Domain Adaptation with Adversarial Residual Transform Networks [arXiv 25 Apr 2018]
- CMD: Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment [arXiv 28 Mar 2018] [Keras(Official)]
- CDAAE: Cross-Domain Adversarial Auto-Encoder[arXiv 17 Apr 2018]
- CPUA: Simple Domain Adaptation with Class Prediction Uncertainty Alignment[arXiv 12 Apr 2018]
- Tran's: Joint Pixel and Feature-level Domain Adaptation in the Wild[arXiv 28 Feb 2018]
- InvAuto: Invertible Autoencoder for domain adaptation[arXiv 10 Feb 2018]