MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels

14 December 2017

Li Fei-Fei

Papers citing "MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels"

20 / 270 papers shown

Title
Are Anchor Points Really Indispensable in Label-Noise Learning? Xiaobo Xia Tongliang Liu N. Wang Bo Han Chen Gong Gang Niu Masashi Sugiyama NoLa 11 369 0 01 Jun 2019
Combating Label Noise in Deep Learning Using Abstention S. Thulasidasan Tanmoy Bhattacharya J. Bilmes Gopinath Chennupati J. Mohd-Yusof NoLa 22 177 0 27 May 2019
Curriculum Loss: Robust Learning and Generalization against Label Corruption Yueming Lyu Ivor W. Tsang NoLa 63 172 0 24 May 2019
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment Chen Huang Shuangfei Zhai Walter A. Talbott Miguel Angel Bautista Shi Sun Carlos Guestrin J. Susskind 29 75 0 15 May 2019
Unsupervised Label Noise Modeling and Loss Correction Eric Arazo Sanchez Diego Ortego Paul Albert Noel E. O'Connor Kevin McGuinness NoLa 44 602 0 25 Apr 2019
Wasserstein Adversarial Regularization (WAR) on label noise Kilian Fatras B. Bushan Sylvain Lobry Rémi Flamary D. Tuia Nicolas Courty 19 24 0 08 Apr 2019
On The Power of Curriculum Learning in Training Deep Networks Guy Hacohen D. Weinshall ODL 24 438 0 07 Apr 2019
IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters Xinshao Wang Yang Hua Elyor Kodirov David A. Clifton N. Robertson NoLa 24 62 0 28 Mar 2019
An Effective Label Noise Model for DNN Text Classification Ishan Jindal Daniel Pressel Brian Lester M. Nokleby NoLa 32 48 0 18 Mar 2019
CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation Yanning Zhou O. F. Onder Qi Dou E. Tsougenis Hao Chen Pheng-Ann Heng 13 196 0 13 Mar 2019
Characterization of Posidonia Oceanica Seagrass Aerenchyma through Whole Slide Imaging: A Pilot Study O. Debeir J. Allard C. Decaestecker J. Hermand 16 3 0 07 Mar 2019
Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges Di Feng Christian Haase-Schuetz Lars Rosenbaum Heinz Hertlein Claudius Gläser Fabian Duffhauss W. Wiesbeck Klaus C. J. Dietmayer 3DPC 41 987 0 21 Feb 2019
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion Ryutaro Tanno A. Saeedi S. Sankaranarayanan Daniel C. Alexander N. Silberman NoLa 27 228 0 10 Feb 2019
Distant Vehicle Detection Using Radar and Vision Simon Chadwick William P. Maddern Paul Newman 21 185 0 30 Jan 2019
An Empirical Study of Example Forgetting during Deep Neural Network Learning Mariya Toneva Alessandro Sordoni Rémi Tachet des Combes Adam Trischler Yoshua Bengio Geoffrey J. Gordon 46 712 0 12 Dec 2018
Theory of Curriculum Learning, with Convex Loss Functions D. Weinshall D. Amir 16 41 0 09 Dec 2018
An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images B. Damodaran Rémi Flamary Vivien Seguy Nicolas Courty NoLa 29 39 0 02 Oct 2018
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data Nan Lu Gang Niu A. Menon Masashi Sugiyama MQ 30 86 0 31 Aug 2018
Dimensionality-Driven Learning with Noisy Labels Xingjun Ma Yisen Wang Michael E. Houle Shuo Zhou S. Erfani Shutao Xia S. Wijewickrema James Bailey NoLa 35 425 0 07 Jun 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 58 2,028 0 18 Apr 2018