Loss factorization, weakly supervised learning and label noise
robustness

Loss factorization, weakly supervised learning and label noise robustness

8 February 2016

Giorgio Patrini

Papers citing "Loss factorization, weakly supervised learning and label noise robustness"

19 / 19 papers shown

Title
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data Hiroshi Takahashi Tomoharu Iwata Atsutoshi Kumagai Yuuki Yamanaka 43 1 0 29 May 2024
DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems Nabeel Seedat F. Imrie M. Schaar 27 12 0 09 Nov 2022
Improving Data Quality with Training Dynamics of Gradient Boosting Decision Trees M. Ponti L. Oliveira Mathias Esteban Valentina Garcia J. Román Luis Argerich TDI 30 4 0 20 Oct 2022
Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels Chuang Zhang Li Shen Jian Yang Chen Gong NoLa 27 5 0 27 Jun 2022
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation De-Chun Cheng Tongliang Liu Yixiong Ning Nannan Wang Bo Han Gang Niu Xinbo Gao Masashi Sugiyama NoLa 39 65 0 06 Jun 2022
Multi-class Label Noise Learning via Loss Decomposition and Centroid Estimation Yongliang Ding Tao Zhou Chuang Zhang Yijing Luo Juan Tang Chen Gong NoLa 32 4 0 21 Mar 2022
Analysing the Noise Model Error for Realistic Noisy Label Data Michael A. Hedderich D. Zhu Dietrich Klakow NoLa 29 19 0 24 Jan 2021
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise Pengfei Chen Junjie Ye Guangyong Chen Jingwei Zhao Pheng-Ann Heng NoLa 40 122 0 10 Dec 2020
A Survey of Label-noise Representation Learning: Past, Present and Future Bo Han Quanming Yao Tongliang Liu Gang Niu Ivor W. Tsang James T. Kwok Masashi Sugiyama NoLa 24 158 0 09 Nov 2020
Calibrated Surrogate Losses for Adversarially Robust Classification Han Bao Clayton Scott Masashi Sugiyama 27 45 0 28 May 2020
Confidence Scores Make Instance-dependent Label-noise Learning Possible Antonin Berthon Bo Han Gang Niu Tongliang Liu Masashi Sugiyama NoLa 37 104 0 11 Jan 2020
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis Davood Karimi Haoran Dou Simon K. Warfield Ali Gholipour NoLa 24 535 0 05 Dec 2019
Confident Learning: Estimating Uncertainty in Dataset Labels Curtis G. Northcutt Lu Jiang Isaac L. Chuang NoLa 38 674 0 31 Oct 2019
Learning with Inadequate and Incorrect Supervision Chen Gong Hengmin Zhang Jian Yang Dacheng Tao 11 33 0 20 Feb 2019
Learning Deep Networks from Noisy Labels with Dropout Regularization Ishan Jindal M. Nokleby Xuewen Chen NoLa 6 183 0 09 May 2017
Positive-Unlabeled Learning with Non-Negative Risk Estimator Ryuichi Kiryo Gang Niu M. C. D. Plessis Masashi Sugiyama 33 468 0 02 Mar 2017
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Giorgio Patrini A. Rozza A. Menon Richard Nock Lizhen Qu NoLa 51 1,435 0 13 Sep 2016
Learning from Binary Labels with Instance-Dependent Corruption A. Menon Brendan van Rooyen Nagarajan Natarajan NoLa 31 41 0 03 May 2016
Double Ramp Loss Based Reject Option Classifier Naresh Manwani Aritra Ghosh P. Sastry Ramasubramanian Sundararajan 40 49 0 26 Nov 2013