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

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

15 January 2021

Mamshad Nayeem Rizve

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

11 / 261 papers shown

Title
Structured Sparse R-CNN for Direct Scene Graph Generation Yao Teng Limin Wang 3DPC GNN 26 53 0 21 Jun 2021
Robust Mutual Learning for Semi-supervised Semantic Segmentation Pan Zhang Bo Zhang Ting Zhang Dong Chen Fang Wen 23 17 0 01 Jun 2021
Motion-Augmented Self-Training for Video Recognition at Smaller Scale Kirill Gavrilyuk Mihir Jain I. Karmanov Cees G. M. Snoek 18 21 0 04 May 2021
Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification Fengbei Liu Yu Tian F. Cordeiro Vasileios Belagiannis Ian Reid G. Carneiro 23 42 0 05 Mar 2021
An Empirical Study and Analysis on Open-Set Semi-Supervised Learning Huixiang Luo Hao Cheng Fanxu Meng Yuting Gao Ke Li Mengdan Zhang Xing Sun 19 8 0 19 Jan 2021
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation Zhedong Zheng Yi Yang NoLa 191 497 0 08 Mar 2020
Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy Angelica I. Aviles-Rivero Nicolas Papadakis Ruoteng Li P. Sellars Samar M. Alsaleh R. Tan Carola-Bibiane Schönlieb 27 3 0 20 Jun 2019
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 180 124 0 29 Nov 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 338 11,684 0 09 Mar 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,661 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015