Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
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Swami Sankaranarayanan | Rama Chellappa | Ser-Nam Lim | Arpit Jain | Yogesh Balaji | R. Chellappa | Ser-Nam Lim | S. Sankaranarayanan | Y. Balaji | Arpit Jain
[1] Rama Chellappa,et al. Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[2] Leon A. Gatys,et al. A Neural Algorithm of Artistic Style , 2015, ArXiv.
[3] Sanja Fidler,et al. The Role of Context for Object Detection and Semantic Segmentation in the Wild , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[4] Dumitru Erhan,et al. Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] 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.
[6] Trevor Darrell,et al. FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation , 2016, ArXiv.
[7] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[8] Philip David,et al. Domain Adaptation for Semantic Segmentation of Urban Scenes , 2017 .
[9] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[10] Roberto Cipolla,et al. Semantic object classes in video: A high-definition ground truth database , 2009, Pattern Recognit. Lett..
[11] Jiasen Lu,et al. Hierarchical Question-Image Co-Attention for Visual Question Answering , 2016, NIPS.
[12] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Michael I. Jordan,et al. Unsupervised Domain Adaptation with Residual Transfer Networks , 2016, NIPS.
[14] Koby Crammer,et al. Analysis of Representations for Domain Adaptation , 2006, NIPS.
[15] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[16] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[17] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[18] Vladlen Koltun,et al. Playing for Data: Ground Truth from Computer Games , 2016, ECCV.
[19] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[20] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[21] Hal Daumé,et al. Frustratingly Easy Domain Adaptation , 2007, ACL.
[22] Bo Geng,et al. DAML: Domain Adaptation Metric Learning , 2011, IEEE Transactions on Image Processing.
[23] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[24] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[25] Antonio M. López,et al. The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[27] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Rama Chellappa,et al. Domain adaptation for object recognition: An unsupervised approach , 2011, 2011 International Conference on Computer Vision.
[29] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Taesung Park,et al. CyCADA: Cycle-Consistent Adversarial Domain Adaptation , 2017, ICML.
[31] Li Fei-Fei,et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.
[32] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[33] Ming-Yu Liu,et al. Coupled Generative Adversarial Networks , 2016, NIPS.