Exploiting Diversity of Unlabeled Data for Label-Efficient
Semi-Supervised Active Learning

Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning

25 July 2022

Nassir Navab

Papers citing "Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning"

7 / 7 papers shown

Title
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling Zongyao Lyu William J. Beksi DRL 15 0 0 23 Aug 2024
MILAN: Milli-Annotations for Lidar Semantic Segmentation Nermin Samet Gilles Puy Oriane Siméoni Renaud Marlet 3DPC 24 0 0 22 Jul 2024
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection Erik Arakelyan Arnav Arora Isabelle Augenstein 12 9 0 01 Jun 2023
Combining Self-labeling with Selective Sampling Jedrzej Kozal Michal Wo'zniak 6 3 0 11 Jan 2023
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost M. Gao Zizhao Zhang Guo-Ding Yu Sercan Ö. Arik L. Davis Tomas Pfister 149 194 0 16 Oct 2019
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results Antti Tarvainen Harri Valpola OOD MoMe 244 1,279 0 06 Mar 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015