Fetching the paper…
Reading the bibliography…
The existing active learning methods select the samples by evaluating the sample's uncertainty or its effect on the diversity of labeled datasets based on different task-specific or model-specific criteria.
Residuals and influence in regression
R Dennis Cook and Sanford Weisberg · 1982
Earlier work this paper cites.
Query by committee
H Sebastian Seung, Manfred Opper, and Haim Sompolinsky · 1992
Earlier work this paper cites.
Heterogeneous uncertainty sampling for supervised learning
David D Lewis and Jason Catlett · 1994
Earlier work this paper cites.
A sequential algorithm for training text classifiers
David D Lewis and William A Gale · 1994
Earlier work this paper cites.
Active learning using pre-clustering
Hieu T Nguyen and Arnold Smeulders · 2004
Earlier work this paper cites.
Margin-based active learning for structured output spaces
Dan Roth and Kevin Small · 2006
Earlier work this paper cites.
An analysis of active learning strategies for sequence labeling tasks
Burr Settles and Mark Craven · 2008
Earlier work this paper cites.
Multi-class active learning for image classification
Ajay J Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Active instance sampling via matrix partition
Yuhong Guo · 2010
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
Earlier work this paper cites.
Active learning
Burr Settles · 2012
Earlier work this paper cites.
A convex optimization framework for active learning
Ehsan Elhamifar, Guillermo Sapiro, Allen Yang, and S Shankar Sasrty · 2013
Earlier work this paper cites.
Latent structured active learning
Wenjie Luo, Alex Schwing, and Raquel Urtasun · 2013
Earlier work this paper cites.
Selecting influential examples: Active learning with expected model output changes
Alexander Freytag, Erik Rodner, and Joachim Denzler · 2014
Cited alongside, same era.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Cited alongside, same era.
Hierarchical subquery evaluation for active learning on a graph
Oisin Mac Aodha, Neill DF Campbell, Jan Kautz, and Gabriel J Brostow · 2014
Cited alongside, same era.
Context aware active learning of activity recognition models
Mahmudul Hasan and Amit K Roy-Chowdhury · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Cited alongside, same era.
Localization-aware active learning for object detection
Chieh-Chi Kao, Teng-Yok Lee, Pradeep Sen, and Ming-Yu Liu · 2018
Later among the works it cites.
Deep active learning for object detection
Soumya Roy, Asim Unmesh, and Vinay P Namboodiri · 2018
Later among the works it cites.
Mmdetection: Open mmlab detection toolbox and benchmark
Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, et al · 2019
Later among the works it cites.
Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem
Matthias Hein, Maksym Andriushchenko, and Julian Bitterwolf · 2019
Later among the works it cites.
On network design spaces for visual recognition
Ilija Radosavovic, Justin Johnson, Saining Xie, Wan-Yen Lo, and Piotr Dollár · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yi Yang, Zhigang Ma, Feiping Nie, Xiaojun Chang, and Alexander G Hauptmann · 2015
Cited alongside, same era.
Second-order stochastic optimization in linear time
Naman Agarwal, Brian Bullins, and Elad Hazan · 2016
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
Cited alongside, same era.
Cost-effective active learning for deep image classification
Keze Wang, Dongyu Zhang, Ya Li, Ruimao Zhang, and Liang Lin · 2016
Cited alongside, same era.
Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
Cited alongside, same era.
Objects365: A large-scale, high-quality dataset for object detection
Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun · 2019
Later among the works it cites.
Variational adversarial active learning
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell · 2019
Later among the works it cites.
Fcos: Fully convolutional one-stage object detection
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He · 2019
Later among the works it cites.
Learning loss for active learning
Donggeun Yoo and In So Kweon · 2019
Later among the works it cites.
Towards fine-grained sampling for active learning in object detection
Sai Vikas Desai and Vineeth N Balasubramanian · 2020
Later among the works it cites.
Scalable active learning for object detection
Elmar Haussmann, Michele Fenzi, Kashyap Chitta, Jan Ivanecky, Hanson Xu, Donna Roy, Akshita Mittel, Nicolas Koumchatzky, Clement Farabet, and Jose M Alvarez · 2020
Later among the works it cites.
A survey of deep active learning
Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang · 2020
Later among the works it cites.
Not all unlabeled data are equal: learning to weight data in semi-supervised learning
Zhongzheng Ren, Raymond A Yeh, and Alexander G Schwing · 2020
Later among the works it cites.