Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild
暂无分享,去创建一个
Kihyuk Sohn | Xiaoming Liu | Xiang Yu | Manmohan Chandraker | Luan Tran | Kihyuk Sohn | Manmohan Chandraker | Xiaoming Liu | Luan Tran | Xiang Yu
[1] James Philbin,et al. FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Colin Raffel,et al. Realistic Evaluation of Deep Semi-Supervised Learning Algorithms , 2018, NeurIPS.
[5] Fan Yang,et al. Good Semi-supervised Learning That Requires a Bad GAN , 2017, NIPS.
[6] Ming-Hsuan Yang,et al. Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[7] Philip S. Yu,et al. Transfer Feature Learning with Joint Distribution Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.
[8] Dumitru Erhan,et al. Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Leonidas J. Guibas,et al. ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.
[10] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[11] Tatsuya Harada,et al. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[12] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[13] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[14] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[15] Xiaoming Liu,et al. Representation Learning by Rotating Your Faces , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Yuxiao Hu,et al. MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition , 2016, ECCV.
[17] Xiaoming Liu,et al. Disentangled Representation Learning GAN for Pose-Invariant Face Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Jitendra Malik,et al. View Synthesis by Appearance Flow , 2016, ECCV.
[19] Laurens van der Maaten,et al. Accelerating t-SNE using tree-based algorithms , 2014, J. Mach. Learn. Res..
[20] Anton Konushin,et al. Evaluation of Traffic Sign Recognition Methods Trained on Synthetically Generated Data , 2013, ACIVS.
[21] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[22] Honglak Lee,et al. Attribute2Image: Conditional Image Generation from Visual Attributes , 2015, ECCV.
[23] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[24] Scott E. Reed,et al. Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis , 2015, NIPS.
[25] Michael I. Jordan,et al. Unsupervised Domain Adaptation with Residual Transfer Networks , 2016, NIPS.
[26] Xiaoou Tang,et al. A large-scale car dataset for fine-grained categorization and verification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Gregory D. Hager,et al. Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Timo Aila,et al. Temporal Ensembling for Semi-Supervised Learning , 2016, ICLR.
[29] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[30] Johannes Stallkamp,et al. The German Traffic Sign Recognition Benchmark: A multi-class classification competition , 2011, The 2011 International Joint Conference on Neural Networks.
[31] Qi Tian,et al. DisturbLabel: Regularizing CNN on the Loss Layer , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[33] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[34] Thomas Brox,et al. Multi-view 3D Models from Single Images with a Convolutional Network , 2015, ECCV.
[35] Michael I. Jordan,et al. Domain Adaptation with Randomized Multilinear Adversarial Networks , 2017, ArXiv.
[36] Lior Wolf,et al. Unsupervised Cross-Domain Image Generation , 2016, ICLR.
[37] Joshua B. Tenenbaum,et al. Deep Convolutional Inverse Graphics Network , 2015, NIPS.
[38] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[39] Trevor Darrell,et al. Adapting Visual Category Models to New Domains , 2010, ECCV.
[40] Qiang Yang,et al. Cross Validation Framework to Choose amongst Models and Datasets for Transfer Learning , 2010, ECML/PKDD.
[41] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[42] George Trigeorgis,et al. Domain Separation Networks , 2016, NIPS.
[43] Harri Valpola,et al. Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.
[44] Kate Saenko,et al. Deep CORAL: Correlation Alignment for Deep Domain Adaptation , 2016, ECCV Workshops.
[45] Trevor Darrell,et al. Simultaneous Deep Transfer Across Domains and Tasks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[46] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[47] Koby Crammer,et al. A theory of learning from different domains , 2010, Machine Learning.
[48] Jonathan Krause,et al. Scalable Annotation of Fine-Grained Categories Without Experts , 2017, CHI.
[49] Taesung Park,et al. CyCADA: Cycle-Consistent Adversarial Domain Adaptation , 2017, ICML.
[50] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[51] Geoffrey French,et al. Self-ensembling for visual domain adaptation , 2017, ICLR.
[52] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[53] Xiang Yu,et al. Unsupervised Domain Adaptation for Distance Metric Learning , 2018, International Conference on Learning Representations.
[54] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[55] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[56] Trevor Darrell,et al. Deep Domain Confusion: Maximizing for Domain Invariance , 2014, CVPR 2014.
[57] Kaiming He,et al. Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[58] Ersin Yumer,et al. Transformation-Grounded Image Generation Network for Novel 3D View Synthesis , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[59] Andrew Zisserman,et al. Spatial Transformer Networks , 2015, NIPS.
[60] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[61] Feng Liu,et al. Towards High-Fidelity Nonlinear 3D Face Morphable Model , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[62] Jonathan Krause,et al. 3D Object Representations for Fine-Grained Categorization , 2013, 2013 IEEE International Conference on Computer Vision Workshops.
[63] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[64] Yoshua Bengio,et al. Semi-supervised Learning by Entropy Minimization , 2004, CAP.
[65] Tomas Pfister,et al. Learning from Simulated and Unsupervised Images through Adversarial Training , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[66] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[67] Koby Crammer,et al. Analysis of Representations for Domain Adaptation , 2006, NIPS.
[68] Fei-Fei Li,et al. Label Efficient Learning of Transferable Representations acrosss Domains and Tasks , 2017, NIPS.
[69] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[70] Daniel Cremers,et al. Associative Domain Adaptation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[71] Xiaoming Liu,et al. Nonlinear 3D Face Morphable Model , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[72] Xiaoming Liu,et al. Coefficients Pose-Variant Input Recogni 8 on Engine Frontalized Output Generator FF-GAN D Discriminator Extreme Pose Input Frontalized Output , 2017 .
[73] Kaiming He,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[74] Clément Farabet,et al. Torch7: A Matlab-like Environment for Machine Learning , 2011, NIPS 2011.
[75] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[76] Zhen Wang,et al. Multi-class Generative Adversarial Networks with the L2 Loss Function , 2016, ArXiv.
[77] Tinne Tuytelaars,et al. Joint cross-domain classification and subspace learning for unsupervised adaptation , 2014, Pattern Recognit. Lett..