Generate to Adapt: Aligning Domains Using Generative Adversarial Networks
暂无分享,去创建一个
Carlos D. Castillo | Swami Sankaranarayanan | Rama Chellappa | Yogesh Balaji | R. Chellappa | S. Sankaranarayanan | Y. Balaji | C. Castillo
[1] Jonathan J. Hull,et al. A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[2] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[3] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[4] Bernhard Schölkopf,et al. Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.
[5] Hal Daumé,et al. Frustratingly Easy Domain Adaptation , 2007, ACL.
[6] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[7] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[8] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[9] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[10] Ivor W. Tsang,et al. Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.
[11] Rama Chellappa,et al. Domain adaptation for object recognition: An unsupervised approach , 2011, 2011 International Conference on Computer Vision.
[12] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[13] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[14] Philip S. Yu,et al. Transfer Feature Learning with Joint Distribution Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.
[15] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[16] Trevor Darrell,et al. Deep Domain Confusion: Maximizing for Domain Invariance , 2014, CVPR 2014.
[17] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[18] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[19] Stefan Carlsson,et al. CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[20] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[21] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[22] James Philbin,et al. FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[24] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[25] Kate Saenko,et al. Learning Deep Object Detectors from 3D Models , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[26] George Trigeorgis,et al. Domain Separation Networks , 2016, NIPS.
[27] Carlos D. Castillo,et al. Frontal to profile face verification in the wild , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).
[28] Mengjie Zhang,et al. Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation , 2016, ECCV.
[29] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Ming-Yu Liu,et al. Coupled Generative Adversarial Networks , 2016, NIPS.
[31] Carlos D. Castillo,et al. Triplet probabilistic embedding for face verification and clustering , 2016, 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS).
[32] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[33] Michael I. Jordan,et al. Unsupervised Domain Adaptation with Residual Transfer Networks , 2016, NIPS.
[34] Lior Wolf,et al. Unsupervised Cross-Domain Image Generation , 2016, ICLR.
[35] Dumitru Erhan,et al. Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Kate Saenko,et al. VisDA: The Visual Domain Adaptation Challenge , 2017, ArXiv.
[37] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Xin Pan,et al. YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Michael I. Jordan,et al. Deep Transfer Learning with Joint Adaptation Networks , 2016, ICML.
[40] Kate Saenko,et al. Synthetic to Real Adaptation with Deep Generative Correlation Alignment Networks , 2017, ArXiv.
[41] Jonathon Shlens,et al. Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.
[42] Kate Saenko,et al. Synthetic to Real Adaptation with Generative Correlation Alignment Networks , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).
[43] Rama Chellappa,et al. HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.