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In recent years, semi-supervised learning has been widely explored and shows excellent data efficiency for 2D data.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 1905
Earlier work this paper cites.
Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring
David Berthelot, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel · 1911
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2001
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2002
Earlier work this paper cites.
A simple semi-supervised learning framework for object detection
Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li, Han Zhang, Chen-Yu Lee, and Tomas Pfister · 2005
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2006
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Humble teacher and eager student: Dual network learning for semi-supervised 2d human pose estimation
Rongchang Xie, Chunyu Wang, Wenjun Zeng, and Yizhou Wang · 2011
Earlier work this paper cites.
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip Torr, and Vladlen Koltun · 2012
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
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Two-stream convolutional networks for action recognition in videos
Karen Simonyan and Andrew Zisserman · 2014
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Multi-view convolutional neural networks for 3d shape recognition
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller · 2015
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3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Convolutional two-stream network fusion for video action recognition
Christoph Feichtenhofer, Axel Pinz, and Andrew Zisserman · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Flownet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
Earlier work this paper cites.
Antti Tarvainen and Harri Valpola · 2017
Earlier work this paper cites.
Flow-guided feature aggregation for video object detection
Xizhou Zhu, Yujie Wang, Jifeng Dai, Lu Yuan, and Yichen Wei · 2017
Cited alongside, same era.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
Cited alongside, same era.
Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D Cubuk, and Ian J Goodfellow · 2018
Cited alongside, same era.
Data distillation: Towards omni-supervised learning
Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, and Kaiming He · 2018
Cited alongside, same era.
Spidercnn: Deep learning on point sets with parameterized convolutional filters
Yifan Xu, Tianqi Fan, Mingye Xu, Long Zeng, and Yu Qiao · 2018
Cited alongside, same era.
Featmatch: Feature-based augmentation for semi-supervised learning
Chia-Wen Kuo, Chih-Yao Ma, Jia-Bin Huang, and Zsolt Kira · 2020
Later among the works it cites.
Semi-supervised semantic segmentation with cross-consistency training
Yassine Ouali, Céline Hudelot, and Myriam Tami · 2020
Later among the works it cites.
Self-supervised learning of point clouds via orientation estimation
Omid Poursaeed, Tianxing Jiang, Han Qiao, Nayun Xu, and Vladimir G Kim · 2020
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Info3d: Representation learning on 3d objects using mutual information maximization and contrastive learning
Aditya Sanghi · 2020
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Dualconvmesh-net: Joint geodesic and euclidean convolutions on 3d meshes
Jonas Schult, Francis Engelmann, Theodora Kontogianni, and Bastian Leibe · 2020
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Foldingnet: Point cloud auto-encoder via deep grid deformation
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian · 2018
Cited alongside, same era.
Meshnet: mesh neural network for 3d shape representation
Yutong Feng, Yifan Feng, Haoxuan You, Xibin Zhao, and Yue Gao · 2019
Cited alongside, same era.
Meshcnn: a network with an edge
Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, and Daniel Cohen-Or · 2019
Cited alongside, same era.
Unsupervised multi-task feature learning on point clouds
Kaveh Hassani and Mike Haley · 2019
Cited alongside, same era.
Deepgcns: Can gcns go as deep as cnns?
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem · 2019
Cited alongside, same era.
Semantic segmentation of 3d lidar data in dynamic scene using semi-supervised learning
Jilin Mei, Biao Gao, Donghao Xu, Wen Yao, Xijun Zhao, and Huijing Zhao · 2019
Cited alongside, same era.
Kpconv: Flexible and deformable convolution for point clouds
Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J Guibas · 2019
Cited alongside, same era.
Charu Sharma and Manohar Kaul · 2020
Later among the works it cites.
Adversarial self-supervised learning for semi-supervised 3d action recognition
Chenyang Si, Xuecheng Nie, Wei Wang, Liang Wang, Tieniu Tan, and Jiashi Feng · 2020
Later among the works it cites.
Semi-supervised 3d shape recognition via multimodal deep co-training
Mofei Song, Yu Liu, and Xiao Fan Liu · 2020
Later among the works it cites.
Cnns on surfaces using rotation-equivariant features
Ruben Wiersma, Elmar Eisemann, and Klaus Hildebrandt · 2020
Later among the works it cites.
Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels
Xun Xu and Gim Hee Lee · 2020
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Exponential moving average normalization for self-supervised and semi-supervised learning
Zhaowei Cai, Avinash Ravichandran, Subhransu Maji, Charless Fowlkes, Zhuowen Tu, and Stefano Soatto · 2021
Closest in time.
Semi-supervised semantic segmentation with cross pseudo supervision
Xiaokang Chen, Yuhui Yuan, Gang Zeng, and Jingdong Wang · 2021
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On data-augmentation and consistency-based semi-supervised learning
Atin Ghosh and Alexandre H Thiery · 2021
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Self-supervised feature learning by cross-modality and cross-view correspondences
Longlong Jing, Ling Zhang, and Yingli Tian · 2021
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3dioumatch: Leveraging iou prediction for semi-supervised 3d object detection
He Wang, Yezhen Cong, Or Litany, Yue Gao, and Leonidas J Guibas · 2021
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Multiview pseudo-labeling for semi-supervised learning from video
Bo Xiong, Haoqi Fan, Kristen Grauman, and Christoph Feichtenhofer · 2021
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