In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning

15 January 2021

Papers citing "In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning"

6 / 6 papers shown

Title
Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment Qijie Ding Jie Yin Daokun Zhang Junbin Gao 17 2 0 05 Jul 2023
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation Zhedong Zheng Yi Yang NoLa 148 434 0 08 Mar 2020
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training Yingda Xia Fengze Liu D. Yang Jinzheng Cai Lequan Yu Zhuotun Zhu Daguang Xu Alan Yuille H. Roth 151 118 0 29 Nov 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 220 9,525 0 09 Mar 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 253 4,940 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 232 8,157 0 06 Jun 2015