Theoretical Proportion Label Perturbation for Learning from Label Proportions in Large Bags

26 August 2024

Papers citing "Theoretical Proportion Label Perturbation for Learning from Label Proportions in Large Bags"

9 / 9 papers shown

Title
MixBag: Bag-Level Data Augmentation for Learning from Label Proportions Takanori Asanomi Shinnosuke Matsuo D. Suehiro Ryoma Bise 56 9 0 17 Aug 2023
Learning from Noisy Labels with Deep Neural Networks: A Survey Hwanjun Song Minseok Kim Dongmin Park Yooju Shin Jae-Gil Lee NoLa 117 998 0 16 Jul 2020
Normalized Loss Functions for Deep Learning with Noisy Labels Xingjun Ma Hanxun Huang Yisen Wang Simone Romano S. Erfani James Bailey NoLa 76 445 0 24 Jun 2020
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 363 519 0 05 Mar 2020
Deep multi-class learning from label proportions Gabriel Dulac-Arnold Neil Zeghidour Marco Cuturi Lucas Beyer Jean-Philippe Vert 60 50 0 30 May 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 89 727 0 28 Jan 2019
Regularizing Neural Networks by Penalizing Confident Output Distributions Gabriel Pereyra George Tucker J. Chorowski Lukasz Kaiser Geoffrey E. Hinton NoLa 165 1,141 0 23 Jan 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 351 4,636 0 10 Nov 2016
Webly Supervised Learning of Convolutional Networks Xinlei Chen Abhinav Gupta SSL 90 373 0 07 May 2015