On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data

31 August 2018

Papers citing "On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data"

26 / 26 papers shown

Title
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 334 508 0 05 Mar 2020
Semi-Supervised Learning via Compact Latent Space Clustering Konstantinos Kamnitsas Daniel Coelho De Castro Loic Le Folgoc Ian Walker Ryutaro Tanno Daniel Rueckert Ben Glocker A. Criminisi A. Nori SSL 55 89 0 07 Jun 2018
Masking: A New Perspective of Noisy Supervision Bo Han Jiangchao Yao Gang Niu Mingyuan Zhou Ivor Tsang Ya Zhang Masashi Sugiyama NoLa 51 254 0 21 May 2018
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 Masashi Sugiyama NoLa 73 2,051 0 18 Apr 2018
Learning to Reweight Examples for Robust Deep Learning Mengye Ren Wenyuan Zeng Binh Yang R. Urtasun OOD NoLa 107 1,419 0 24 Mar 2018
Classification from Pairwise Similarity and Unlabeled Data Han Bao Gang Niu Masashi Sugiyama 188 88 0 12 Feb 2018
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels Lu Jiang Zhengyuan Zhou Thomas Leung Li Li Li Fei-Fei NoLa 83 1,448 0 14 Dec 2017
Smooth Neighbors on Teacher Graphs for Semi-supervised Learning Yucen Luo Jun Zhu Mengxi Li Yong Ren Bo Zhang 40 242 0 01 Nov 2017
Learning with Bounded Instance- and Label-dependent Label Noise Jiacheng Cheng Tongliang Liu K. Ramamohanarao Dacheng Tao NoLa 57 148 0 12 Sep 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 96 8,807 0 25 Aug 2017
Positive-Unlabeled Learning with Non-Negative Risk Estimator Ryuichi Kiryo Gang Niu M. C. D. Plessis Masashi Sugiyama 55 471 0 02 Mar 2017
Learning Discrete Representations via Information Maximizing Self-Augmented Training Weihua Hu Takeru Miyato Seiya Tokui Eiichi Matsumoto Masashi Sugiyama 56 446 0 28 Feb 2017
Temporal Ensembling for Semi-Supervised Learning S. Laine Timo Aila UQCV 150 2,543 0 07 Oct 2016
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Giorgio Patrini A. Rozza A. Menon Richard Nock Zhuang Li NoLa 75 1,444 0 13 Sep 2016
Learning from Binary Labels with Instance-Dependent Corruption A. Menon Brendan van Rooyen Nagarajan Natarajan NoLa 66 41 0 03 May 2016
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning Gang Niu M. C. D. Plessis Tomoya Sakai Yao Ma Masashi Sugiyama 39 127 0 10 Mar 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.1K 192,638 0 10 Dec 2015
Distributional Smoothing with Virtual Adversarial Training Takeru Miyato S. Maeda Masanori Koyama Ken Nakae S. Ishii 66 458 0 02 Jul 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 228 43,154 0 11 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 535 149,474 0 22 Dec 2014
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 137 4,653 0 21 Dec 2014
Training Deep Neural Networks on Noisy Labels with Bootstrapping Scott E. Reed Honglak Lee Dragomir Anguelov Christian Szegedy D. Erhan Andrew Rabinovich NoLa 91 1,014 0 20 Dec 2014
Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances M. C. D. Plessis Masashi Sugiyama SSL 48 21 0 01 May 2013
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising Gilles Blanchard Marek Flaska G. Handy Sara Pozzi Clayton Scott NoLa 46 243 0 05 Mar 2013
Information-Maximization Clustering based on Squared-Loss Mutual Information Masashi Sugiyama M. Yamada Manabu Kimura Hirotaka Hachiya 62 45 0 03 Dec 2011
Composite Binary Losses Mark D. Reid Robert C. Williamson 83 223 0 17 Dec 2009