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A Meta-Learning Approach to One-Step Active Learning
v1v2 (latest)

A Meta-Learning Approach to One-Step Active Learning

26 June 2017
Gabriella Contardo
Ludovic Denoyer
Thierry Artières
ArXiv (abs)PDFHTML

Papers citing "A Meta-Learning Approach to One-Step Active Learning"

15 / 15 papers shown
DiffusAL: Coupling Active Learning with Graph Diffusion for
  Label-Efficient Node Classification
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification
Sandra Gilhuber
Julian Busch
Daniel Rotthues
Christian M. m. Frey
Thomas Seidl
DiffM
245
1
0
31 Jul 2023
Taming Small-sample Bias in Low-budget Active Learning
Taming Small-sample Bias in Low-budget Active Learning
Linxin Song
Jieyu Zhang
Xiaotian Lu
Wanrong Zhu
AI4CE
219
0
0
19 Jun 2023
A Survey of Dataset Refinement for Problems in Computer Vision Datasets
A Survey of Dataset Refinement for Problems in Computer Vision DatasetsACM Computing Surveys (ACM CSUR), 2022
Zhijing Wan
Zhixiang Wang
CheukTing Chung
Zheng Wang
341
16
0
21 Oct 2022
Partition-Based Active Learning for Graph Neural Networks
Partition-Based Active Learning for Graph Neural Networks
Jiaqi Ma
Ziqiao Ma
Joyce Chai
Qiaozhu Mei
189
19
0
23 Jan 2022
Low Budget Active Learning via Wasserstein Distance: An Integer
  Programming Approach
Low Budget Active Learning via Wasserstein Distance: An Integer Programming ApproachInternational Conference on Learning Representations (ICLR), 2021
Rafid Mahmood
Sanja Fidler
M. Law
246
40
0
05 Jun 2021
MedSelect: Selective Labeling for Medical Image Classification Combining
  Meta-Learning with Deep Reinforcement Learning
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning
Akshay Smit
Damir Vrabac
Yujie He
A. Ng
Andrew L. Beam
Pranav Rajpurkar
165
7
0
26 Mar 2021
A Survey on Active Deep Learning: From Model-driven to Data-driven
Peng Liu
Lizhe Wang
Guojin He
Lei Zhao
251
20
0
25 Jan 2021
Towards Understanding the Behaviors of Optimal Deep Active Learning
  Algorithms
Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms
Yilun Zhou
Adithya Renduchintala
Xian Li
Sida Wang
Yashar Mehdad
Asish Ghoshal
FedML
99
0
0
29 Dec 2020
Learning to Sample the Most Useful Training Patches from Images
Learning to Sample the Most Useful Training Patches from Images
Shuyang Sun
Liang Chen
Greg Slabaugh
Juil Sock
165
10
0
24 Nov 2020
Reinforced active learning for image segmentation
Reinforced active learning for image segmentationInternational Conference on Learning Representations (ICLR), 2020
Arantxa Casanova
Pedro H. O. Pinheiro
Negar Rostamzadeh
C. Pal
209
119
0
16 Feb 2020
Picking groups instead of samples: A close look at Static Pool-based
  Meta-Active Learning
Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning
Ignasi Mas
J. Morros
Verónica Vilaplana
123
2
0
01 Nov 2019
Active Learning for Graph Neural Networks via Node Feature Propagation
Active Learning for Graph Neural Networks via Node Feature Propagation
Yuexin Wu
Yichong Xu
Aarti Singh
Yiming Yang
A. Dubrawski
GNNAI4CE
174
71
0
16 Oct 2019
Discovering General-Purpose Active Learning Strategies
Discovering General-Purpose Active Learning Strategies
Ksenia Konyushkova
Raphael Sznitman
Pascal Fua
156
36
0
09 Oct 2018
Visual Curiosity: Learning to Ask Questions to Learn Visual Recognition
Visual Curiosity: Learning to Ask Questions to Learn Visual Recognition
Jianwei Yang
Jiasen Lu
Stefan Lee
Dhruv Batra
Devi Parikh
185
42
0
01 Oct 2018
Single Shot Active Learning using Pseudo Annotators
Single Shot Active Learning using Pseudo Annotators
Yazhou Yang
Marco Loog
VLM
155
30
0
17 May 2018
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