Improving Domain-Specific Classification by Collaborative Learning with Adaptation Networks
暂无分享,去创建一个
Hau-San Wong | Si Wu | Zhiwen Yu | Rui Li | Wen Ming Cao | Jian Zhong
[1] Fabio Maria Carlucci,et al. From Source to Target and Back: Symmetric Bi-Directional Adaptive GAN , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[2] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[3] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[5] Daniel Cremers,et al. Associative Domain Adaptation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[6] Pedro H. O. Pinheiro,et al. Unsupervised Domain Adaptation with Similarity Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[7] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[8] Geoffrey French,et al. Self-ensembling for domain adaptation , 2017, ArXiv.
[9] Jonathan J. Hull,et al. A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[10] Stefano Ermon,et al. A DIRT-T Approach to Unsupervised Domain Adaptation , 2018, ICLR.
[11] Tatsuya Harada,et al. Asymmetric Tri-training for Unsupervised Domain Adaptation , 2017, ICML.
[12] Carlos D. Castillo,et al. Generate to Adapt: Aligning Domains Using Generative Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[13] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[14] Ming-Yu Liu,et al. Coupled Generative Adversarial Networks , 2016, NIPS.
[15] Honglak Lee,et al. An Analysis of Single-Layer Networks in Unsupervised Feature Learning , 2011, AISTATS.
[16] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[17] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[18] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[19] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[20] Sethuraman Panchanathan,et al. Deep Hashing Network for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] George Trigeorgis,et al. Domain Separation Networks , 2016, NIPS.
[22] Mengjie Zhang,et al. Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation , 2016, ECCV.
[23] Dumitru Erhan,et al. Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[25] Tatsuya Harada,et al. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[26] Kate Saenko,et al. VisDA: The Visual Domain Adaptation Challenge , 2017, ArXiv.
[27] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[29] Jan Kautz,et al. Unsupervised Image-to-Image Translation Networks , 2017, NIPS.
[30] Taesung Park,et al. CyCADA: Cycle-Consistent Adversarial Domain Adaptation , 2017, ICML.