A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is
Critical for Semi-supervised Classification

A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification

27 March 2024

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Papers citing "A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification"

16 / 16 papers shown

Title
Generating Unbiased Pseudo-labels via a Theoretically Guaranteed Chebyshev Constraint to Unify Semi-supervised Classification and Regression Jiaqi Wu Junbiao Pang Qingming Huang 34 1 0 03 Nov 2023
Bias Mimicking: A Simple Sampling Approach for Bias Mitigation Maan Qraitem Kate Saenko Bryan A. Plummer 84 34 0 30 Sep 2022
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning Yidong Wang Hao Chen Qiang Heng Wenxin Hou Yue Fan ... Marios Savvides T. Shinozaki Bhiksha Raj Bernt Schiele Xing Xie 219 278 0 15 May 2022
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling Bowen Zhang Yidong Wang Wenxin Hou Hao Wu Jindong Wang Manabu Okumura T. Shinozaki AAML 335 895 0 15 Oct 2021
Humble Teachers Teach Better Students for Semi-Supervised Object Detection Yihe Tang Weifeng Chen Yijun Luo Yuting Zhang 70 183 0 19 Jun 2021
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation Zhedong Zheng Yi Yang NoLa 236 505 0 08 Mar 2020
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence Kihyuk Sohn David Berthelot Chun-Liang Li Zizhao Zhang Nicholas Carlini E. D. Cubuk Alexey Kurakin Han Zhang Colin Raffel AAML 160 3,572 0 21 Jan 2020
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring David Berthelot Nicholas Carlini E. D. Cubuk Alexey Kurakin Kihyuk Sohn Han Zhang Colin Raffel 97 683 0 21 Nov 2019
RandAugment: Practical automated data augmentation with a reduced search space E. D. Cubuk Barret Zoph Jonathon Shlens Quoc V. Le MQ 250 3,502 0 30 Sep 2019
Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning Zhanghan Ke Daoye Wang Qiong Yan Jimmy S. J. Ren Rynson W. H. Lau 45 215 0 03 Sep 2019
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning Eric Arazo Diego Ortego Paul Albert Noel E. O'Connor Kevin McGuinness 121 843 0 08 Aug 2019
MixMatch: A Holistic Approach to Semi-Supervised Learning David Berthelot Nicholas Carlini Ian Goodfellow Nicolas Papernot Avital Oliver Colin Raffel 151 3,033 0 06 May 2019
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Takeru Miyato S. Maeda Masanori Koyama S. Ishii GAN 151 2,738 0 13 Apr 2017
Temporal Ensembling for Semi-Supervised Learning S. Laine Timo Aila UQCV 185 2,567 0 07 Oct 2016
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning Mehdi S. M. Sajjadi Mehran Javanmardi Tolga Tasdizen BDL 85 1,115 0 14 Jun 2016
Learning with Pseudo-Ensembles Philip Bachman O. Alsharif Doina Precup 89 601 0 16 Dec 2014