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Semi-supervised learning, i.e., training networks with both labeled and unlabeled data, has made significant progress recently.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
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Semi-supervised self-training of object detection models
Chuck Rosenberg, Martial Hebert, and Henry Schneiderman · 2005
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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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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Lsda: Large scale detection through adaptation
Judy Hoffman, Sergio Guadarrama, Eric S Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, and Kate Saenko · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Watch and learn: Semi-supervised learning for object detectors from video
Ishan Misra, Abhinav Shrivastava, and Martial Hebert · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
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Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
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Large scale semi-supervised object detection using visual and semantic knowledge transfer
Yuxing Tang, Josiah Wang, Boyang Gao, Emmanuel Dellandréa, Robert Gaizauskas, and Liming Chen · 2016
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
Cited alongside, same era.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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Yolo9000: better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Note-rcnn: Noise tolerant ensemble rcnn for semi-supervised object detection
Jiyang Gao, Jiang Wang, Shengyang Dai, Li-Jia Li, and Ram Nevatia · 2019
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Mixup as locally linear out-of-manifold regularization
Hongyu Guo, Yongyi Mao, and Richong Zhang · 2019
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Consistency-based semi-supervised learning for object detection
Jisoo Jeong, Seungeui Lee, Jeesoo Kim, and Nojun Kwak · 2019
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Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
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Tangent-normal adversarial regularization for semi-supervised learning
Bing Yu, Jingfeng Wu, Jinwen Ma, and Zhanxing Zhu · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Antti Tarvainen and Harri Valpola · 2017
Cited alongside, same era.
Random erasing data augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang · 2017
Cited alongside, same era.
Cascade r-cnn: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
Cited alongside, same era.
Acquisition of localization confidence for accurate object detection
Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, and Yuning Jiang · 2018
Cited alongside, same era.
Cornernet: Detecting objects as paired keypoints
Hei Law and Jia Deng · 2018
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.
Deep co-training for semi-supervised image recognition
Siyuan Qiao, Wei Shen, Zhishuai Zhang, Bo Wang, and Alan Yuille · 2018
Cited alongside, same era.
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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 · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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AugMix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
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Featmatch: Feature-based augmentation for semi-supervised learning
Chia-Wen Kuo, Chih-Yao Ma, Jia-Bin Huang, and Zsolt Kira · 2020
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Overcoming classifier imbalance for long-tail object detection with balanced group softmax
Yu Li, Tao Wang, Bingyi Kang, Sheng Tang, Chunfeng Wang, Jintao Li, and Jiashi Feng · 2020
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Imbalance problems in object detection: A review
Kemal Oksuz, Baris Can Cam, Sinan Kalkan, and Emre Akbas · 2020
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Proposal learning for semi-supervised object detection
Peng Tang, Chetan Ramaiah, Ran Xu, and Caiming Xiong · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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