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Recent semi-supervised learning (SSL) methods are commonly based on pseudo labeling.
Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky · 1995
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Microsoft coco: Common objects in context
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Dropout: a simple way to prevent neural networks from overfitting
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Deep residual learning for image recognition
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Deep networks with stochastic depth
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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Weight-averaged, consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Antti Tarvainen and Harri Valpola · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Leveraging uncertainty estimates for predicting segmentation quality
Terrance DeVries and Graham W Taylor · 2018
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018
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Adversarial learning for semi-supervised semantic segmentation
Wei-Chih Hung, Yi-Hsuan Tsai, Yan-Ting Liou, Yen-Yu Lin, and Ming-Hsuan Yang · 2018
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Semi-supervised skin lesion segmentation via transformation consistent self-ensembling model
Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, and Pheng-Ann Heng · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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Deep semi-supervised segmentation with weight-averaged consistency targets
Christian S Perone and Julien Cohen-Adad · 2018
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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 · 2019
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
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Consistency regularization and cutmix for semi-supervised semantic segmentation
Geoffrey French, Timo Aila, Samuli Laine, Michal Mackiewicz, and Graham Finlayson · 2019
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Semi-supervised semantic segmentation needs strong, high-dimensional perturbations
Geoff French, Timo Aila, Samuli Laine, Michal Mackiewicz, and Graham Finlayson · 2019
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Semi-supervised semantic segmentation needs strong, varied perturbations
Geoffrey French, Samuli Laine, Timo Aila, Michal Mackiewicz, and Graham Finlayson · 2020
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Yixiao Ge, Dapeng Chen, and Hongsheng Li · 2020
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Self-training for end-to-end speech recognition
Jacob Kahn, Ann Lee, and Awni Hannun · 2020
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A three-stage self-training framework for semi-supervised semantic segmentation
Rihuan Ke, Angelica Aviles-Rivero, Saurabh Pandey, Saikumar Reddy, and Carola-Bibiane Schönlieb · 2020
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Guided collaborative training for pixel-wise semi-supervised learning
Zhanghan Ke, Kaican Li Di Qiu, Qiong Yan, and Rynson WH Lau · 2020
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Junxian He, Jiatao Gu, Jiajun Shen, and Marc’Aurelio Ranzato · 2019
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Adversarial learning for semi-supervised semantic segmentation
Wei Chih Hung, Yi Hsuan Tsai, Yan Ting Liou, Yen-Yu Lin, and Ming Hsuan Yang · 2019
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Dual student: Breaking the limits of the teacher in semi-supervised learning
Zhanghan Ke, Daoye Wang, Qiong Yan, Jimmy Ren, and Rynson WH Lau · 2019
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Semi-supervised semantic segmentation with high-and low-level consistency
Sudhanshu Mittal, Maxim Tatarchenko, and Thomas Brox · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Interpolation consistency training for semi-supervised learning
Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Yoshua Bengio, and David Lopez-Paz · 2019
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le · 2019
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Billion-scale semi-supervised learning for image classification
I Zeki Yalniz, Hervé Jégou, Kan Chen, Manohar Paluri, and Dhruv Mahajan · 2019
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Semi-supervised segmentation based on error-correcting supervision
Robert Mendel, Luis Antonio de Souza, David Rauber, João Paulo Papa, and Christoph Palm · 2020
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Semi-supervised semantic segmentation with cross-consistency training
Yassine Ouali, Céline Hudelot, and Myriam Tami · 2020
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Improved noisy student training for automatic speech recognition
Daniel S Park, Yu Zhang, Ye Jia, Wei Han, Chung-Cheng Chiu, Bo Li, Yonghui Wu, and Quoc V Le · 2020
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Hieu Pham, Zihang Dai, Qizhe Xie, Minh-Thang Luong, and Quoc V Le · 2020
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On the uncertainty of self-supervised monocular depth estimation
Matteo Poggi, Filippo Aleotti, Fabio Tosi, and Stefano Mattoccia · 2020
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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 · 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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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al · 2020
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Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D Cubuk, and Quoc V Le · 2020
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Pseudoseg: Designing pseudo labels for semantic segmentation
Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, and Tomas Pfister · 2020
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Mask-based data augmentation for semi-supervised semantic segmentation
Ying Chen, Xu Ouyang, Kaiyue Zhu, and Gady Agam · 2021
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Selfmatch: Combining contrastive self-supervision and consistency for semi-supervised learning
Byoungjip Kim, Jinho Choo, Yeong-Dae Kwon, Seongho Joe, Seungjai Min, and Youngjune Gwon · 2021
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Classmix: Segmentation-based data augmentation for semi-supervised learning
Viktor Olsson, Wilhelm Tranheden, Juliano Pinto, and Lennart Svensson · 2021
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Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, and Mubarak Shah · 2021
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A simple baseline for semi-supervised semantic segmentation with strong data augmentation
Jianlong Yuan, Yifan Liu, Chunhua Shen, Zhibin Wang, and Hao Li · 2021
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Pan Zhang, Bo Zhang, Ting Zhang, Dong Chen, Yong Wang, and Fang Wen · 2021
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Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation
Zhedong Zheng and Yi Yang · 2021
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