Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach
暂无分享,去创建一个
Vladimir Pavlovic | Behnam Gholami | Ognjen Rudovic | Pritish Sahu | Konstantinos Bousmalis | Konstantinos Bousmalis | V. Pavlovic | Ognjen Rudovic | Pritish Sahu | Behnam Gholami
[1] Ivor W. Tsang,et al. Multi-class Heterogeneous Domain Adaptation , 2019, J. Mach. Learn. Res..
[2] Sethuraman Panchanathan,et al. Deep Hashing Network for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Debasmit Das,et al. Graph Matching and Pseudo-Label Guided Deep Unsupervised Domain Adaptation , 2018, ICANN.
[5] Kou Tanaka,et al. StarGAN-VC: non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks , 2018, 2018 IEEE Spoken Language Technology Workshop (SLT).
[6] Bo Wang,et al. Moment Matching for Multi-Source Domain Adaptation , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[7] Alexander A. Alemi,et al. Uncertainty in the Variational Information Bottleneck , 2018, ArXiv.
[8] Anton van den Hengel,et al. Infinite Variational Autoencoder for Semi-Supervised Learning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Vladimir Pavlovic,et al. Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[10] Trevor Darrell,et al. Factorized Orthogonal Latent Spaces , 2010, AISTATS.
[11] Ming-Yu Liu,et al. Coupled Generative Adversarial Networks , 2016, NIPS.
[12] Shih-Fu Chang,et al. Deep Transfer Network: Unsupervised Domain Adaptation , 2015, ArXiv.
[13] Takeo Kanade,et al. Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.
[14] Nicolas Courty,et al. Optimal Transport for Domain Adaptation , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] Cheng Wu,et al. Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation , 2018, IEEE Transactions on Image Processing.
[16] John Scott Bridle,et al. Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition , 1989, NATO Neurocomputing.
[17] Shakir Mohamed,et al. Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning , 2015, NIPS.
[18] Jung-Woo Ha,et al. StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[19] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[20] Geoffrey J. Gordon,et al. Deep Generative and Discriminative Domain Adaptation , 2019, AAMAS.
[21] Tatsuya Harada,et al. Asymmetric Tri-training for Unsupervised Domain Adaptation , 2017, ICML.
[22] Yongxin Yang,et al. Deeper, Broader and Artier Domain Generalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[23] Alexander A. Alemi,et al. Fixing a Broken ELBO , 2017, ICML.
[24] Naftali Tishby,et al. Deep learning and the information bottleneck principle , 2015, 2015 IEEE Information Theory Workshop (ITW).
[25] Alexander A. Alemi,et al. Deep Variational Information Bottleneck , 2017, ICLR.
[26] Vladimir Pavlovic,et al. Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Dumitru Erhan,et al. Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Jing Zhang,et al. Joint Geometrical and Statistical Alignment for Visual Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[30] Bo Geng,et al. DAML: Domain Adaptation Metric Learning , 2011, IEEE Transactions on Image Processing.
[31] José M. F. Moura,et al. Multiple Source Domain Adaptation with Adversarial Training of Neural Networks , 2017, ArXiv.
[32] Olivier Marre,et al. Relevant sparse codes with variational information bottleneck , 2016, NIPS.
[33] Lior Wolf,et al. One-Sided Unsupervised Domain Mapping , 2017, NIPS.
[34] Wei-Shi Zheng,et al. MIXGAN: Learning Concepts from Different Domains for Mixture Generation , 2018, IJCAI.
[35] Jan Kautz,et al. Unsupervised Image-to-Image Translation Networks , 2017, NIPS.
[36] Yun Fu,et al. Semi-supervised Deep Domain Adaptation via Coupled Neural Networks , 2018, IEEE Transactions on Image Processing.
[37] Kate Saenko,et al. Deep CORAL: Correlation Alignment for Deep Domain Adaptation , 2016, ECCV Workshops.
[38] Stefano Soatto,et al. Information Dropout: Learning Optimal Representations Through Noisy Computation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[39] Vladimir Pavlovic,et al. PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[40] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[41] Namil Kim,et al. Pixel-Level Domain Transfer , 2016, ECCV.
[42] Zhiguo Cao,et al. An Embarrassingly Simple Approach to Visual Domain Adaptation , 2018, IEEE Transactions on Image Processing.
[43] Philip S. Yu,et al. Deep Learning of Transferable Representation for Scalable Domain Adaptation , 2016, IEEE Transactions on Knowledge and Data Engineering.
[44] Tinne Tuytelaars,et al. Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.
[45] Luc Van Gool,et al. ComboGAN: Unrestrained Scalability for Image Domain Translation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[46] Tatsuya Harada,et al. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[47] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[48] Kate Saenko,et al. Return of Frustratingly Easy Domain Adaptation , 2015, AAAI.
[49] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[50] Shaogang Gong,et al. Unsupervised Domain Adaptation for Zero-Shot Learning , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[51] Zhe Gan,et al. Adversarial Symmetric Variational Autoencoder , 2017, NIPS.
[52] Gabriela Csurka,et al. A Comprehensive Survey on Domain Adaptation for Visual Applications , 2017, Domain Adaptation in Computer Vision Applications.
[53] Andrea Vedaldi,et al. Learning multiple visual domains with residual adapters , 2017, NIPS.
[54] Quinn Jones,et al. Few-Shot Adversarial Domain Adaptation , 2017, NIPS.
[55] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[56] Jianhua Lin,et al. Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.
[57] George Trigeorgis,et al. Domain Separation Networks , 2016, NIPS.
[58] Philip S. Yu,et al. Transfer Joint Matching for Unsupervised Domain Adaptation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[59] Edwin Lughofer,et al. Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning , 2017, ICLR.
[60] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[61] Debasmit Das,et al. Sample-to-Sample Correspondence for Unsupervised Domain Adaptation , 2018, Eng. Appl. Artif. Intell..
[62] David Barber,et al. The IM algorithm: a variational approach to Information Maximization , 2003, NIPS 2003.
[63] Qilong Wang,et al. Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[64] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[65] Samy Bengio,et al. Generating Sentences from a Continuous Space , 2015, CoNLL.
[66] Nicolas Courty,et al. Joint distribution optimal transportation for domain adaptation , 2017, NIPS.
[67] Debasmit Das,et al. Unsupervised Domain Adaptation Using Regularized Hyper-Graph Matching , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).