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Deep active learning aims to reduce the annotation cost for the training of deep models, which is notoriously data-hungry.
An analysis of approximations for maximizing submodular set functions—i
George L Nemhauser, Laurence A Wolsey, and Marshall L Fisher · 1978
Earlier work this paper cites.
Nc-approximation schemes for np-and pspace-hard problems for geometric graphs
Harry B III Hunt, Madhav V Marathe, Venkatesh Radhakrishnan, Shankar S Ravi, Daniel J Rosenkrantz, and Richard E Stearns · 1998
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Efficient approximation schemes for geometric problems?
Dániel Marx · 2005
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Beyond novelty detection: Incongruent events, when general and specific classifiers disagree
Daphna Weinshall, Hynek Hermansky, Alon Zweig, Jie Luo, Holly Jimison, Frank Ohl, and Misha Pavel · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Facility location: concepts, models, algorithms and case studies
Reza Zanjirani Farahani and Masoud Hekmatfar · 2009
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Multi-class active learning for image classification
Ajay J Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Why label when you can search? alternatives to active learning for applying human resources to build classification models under extreme class imbalance
Josh Attenberg and Foster Provost · 2010
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Cold-start active learning with robust ordinal matrix factorization
Neil Houlsby, José Miguel Hernández-Lobato, and Zoubin Ghahramani · 2014
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Submodular function maximization
Andreas Krause and Daniel Golovin · 2014
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Submodularity in data subset selection and active learning
Kai Wei, Rishabh Iyer, and Jeff Bilmes · 2015
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Multi-class active learning by uncertainty sampling with diversity maximization
Yi Yang, Zhigang Ma, Feiping Nie, Xiaojun Chang, and Alexander G Hauptmann · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A batch-mode active learning framework by querying discriminative and representative samples for hyperspectral image classification
Zengmao Wang, Bo Du, Lefei Zhang, and Liangpei Zhang · 2016
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Deep active learning over the long tail
Yonatan Geifman and Ran El-Yaniv · 2017
Cited alongside, same era.
Deep active learning for image classification
Hiranmayi Ranganathan, Hemanth Venkateswara, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
Cited alongside, same era.
Deep similarity-based batch mode active learning with exploration-exploitation
Changchang Yin, Buyue Qian, Shilei Cao, Xiaoyu Li, Jishang Wei, Qinghua Zheng, and Ian Davidson · 2017
Cited alongside, same era.
The power of ensembles for active learning in image classification
William H Beluch, Tim Genewein, Andreas Nürnberger, and Jan M Köhler · 2018
Cited alongside, same era.
Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
Cited alongside, same era.
Consistency-based semi-supervised active learning: Towards minimizing labeling cost
Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan Ö Arık, Larry S Davis, and Tomas Pfister · 2020
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Deep active learning with augmentation-based consistency estimation
SeulGi Hong, Heonjin Ha, Junmo Kim, and Min-Kook Choi · 2020
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Towards robust and reproducible active learning using neural networks
Prateek Munjal, N. Hayat, Munawar Hayat, J. Sourati, and S. Khan · 2020
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Deep active learning: Unified and principled method for query and training
Changjian Shui, Fan Zhou, Christian Gagné, and Boyu Wang · 2020
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Scan: Learning to classify images without labels
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool · 2020
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Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Cited alongside, same era.
Discriminative active learning
Daniel Gissin and Shai Shalev-Shwartz · 2019
Cited alongside, same era.
Towards better uncertainty sampling: Active learning with multiple views for deep convolutional neural network
Tao He, Xiaoming Jin, Guiguang Ding, Lan Yi, and Chenggang Yan · 2019
Cited alongside, same era.
Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Andreas Kirsch, Joost Van Amersfoort, and Yarin Gal · 2019
Cited alongside, same era.
Parting with illusions about deep active learning
Sudhanshu Mittal, Maxim Tatarchenko, Özgün Çiçek, and Thomas Brox · 2019
Cited alongside, same era.
Variational adversarial active learning
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell · 2019
Cited alongside, same era.
Learning loss for active learning
Donggeun Yoo and In So Kweon · 2019
Cited alongside, same era.
Cold-start active learning through self-supervised language modeling
Michelle Yuan, Hsuan-Tien Lin, and Jordan L. Boyd-Graber · 2020
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Reducing label effort: Self-supervised meets active learning
Javad Zolfaghari Bengar, Joost van de Weijer, Bartlomiej Twardowski, and Bogdan Raducanu · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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On the marginal benefit of active learning: Does self-supervision eat its cake?
Yao-Chun Chan, Mingchen Li, and Samet Oymak · 2021
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On initial pools for deep active learning
Akshay L Chandra, Sai Vikas Desai, Chaitanya Devaguptapu, and Vineeth N Balasubramanian · 2021
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Low budget active learning via wasserstein distance: An integer programming approach
Rafid Mahmood, Sanja Fidler, and Marc T Law · 2021
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A simple baseline for low-budget active learning
Kossar Pourahmadi, Parsa Nooralinejad, and Hamed Pirsiavash · 2021
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Rethinking deep active learning: Using unlabeled data at model training
Oriane Siméoni, Mateusz Budnik, Yannis Avrithis, and Guillaume Gravier · 2021
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Making your first choice: To address cold start problem in vision active learning
Liangyu Chen, Yutong Bai, Siyu Huang, Yongyi Lu, Bihan Wen, Alan L Yuille, and Zongwei Zhou · 2022
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Active learning on a budget: Opposite strategies suit high and low budgets
Guy Hacohen, Avihu Dekel, and Daphna Weinshall · 2022
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Active self-semi-supervised learning for few labeled samples fast training
Ziting Wen, Oscar Pizarro, and Stefan Williams · 2022
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Yue Yu, Rongzhi Zhang, Ran Xu, Jieyu Zhang, Jiaming Shen, and Chao Zhang · 2022
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