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.

• 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)]
• 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]
• DeppJDOT: DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation [arXiv 27 Mar 2018] [ECCV2018]
• DiDA: DiDA: Disentangled Synthesis for Domain Adaptation [arXiv 21 Mar 2018] [Pytorch(Official)]
• Saito's: Open Set Domain Adaptation by Backpropagation [arXiv 27 Apr 2018] [ECCV2018] [TensorFlow] [Pytorch]
• 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)]
• 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)]
• DA_assoc: 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]

• DAN: Transferable Representation Learning with Deep Adaptation Networks [TPAMI2018]
• 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)]

• transferlearning
• awsome-domain-adaptation
• awesome-transfer-learning