ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks

7 May 2020

Papers citing "ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks"

7 / 7 papers shown

Title
Class-Level Logit Perturbation Mengyang Li Fengguang Su O. Wu Tianjin University AAML 29 3 0 13 Sep 2022
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling Boshen Zhang Yuxi Li Yuanpeng Tu Jinlong Peng Yabiao Wang Cunlin Wu Yanghua Xiao Cairong Zhao NoLa 31 6 0 23 Aug 2022
Large Loss Matters in Weakly Supervised Multi-Label Classification Youngwook Kim Jae Myung Kim Zeynep Akata Jungwook Lee NoLa 24 46 0 08 Jun 2022
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels F. Cordeiro Vasileios Belagiannis Ian Reid G. Carneiro NoLa 22 18 0 22 Oct 2021
FedADC: Accelerated Federated Learning with Drift Control Emre Ozfatura Kerem Ozfatura Deniz Gunduz FedML 30 37 0 16 Dec 2020
Local Label Point Correction for Edge Detection of Overlapping Cervical Cells Jiawei Liu Huijie Fan Qiang Wang Wentao Li Yandong Tang Danbo Wang Mingyi Zhou Li Chen 13 9 0 05 Oct 2020
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 310 497 0 05 Mar 2020