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Active Learning (AL) techniques aim to minimize the training data required to train a model for a given task.
A Mathematical Theory of Communication
Claude E. Shannon and Warren Weaver · 1963
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A sequential algorithm for training text classifiers
David D Lewis and William A Gale · 1994
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Active hidden markov models for information extraction, 2001
Tobias Scheffer, Christian Decomain, and Stefan Wrobel · 2001
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Using cluster-based sampling to select initial training set for active learning in text classification
Jaeho Kang, Kwang Ryu, and Hyuk-Chul Kwon · 2004
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Multiple-instance active learning
Burr Settles, Mark Craven, and Soumya Ray · 2007
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Active learning literature survey
Burr Settles · 2009
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Exploration vs. exploitation in active learning : A bayesian approach
Alexis Bondu, V. Lemaire, and M. Boullé · 2010
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Off to a good start: Using clustering to select the initial training set in active learning
Rong Hu, Brian Mac Namee, and Sarah Jane Delany · 2010
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Two faces of active learning
S. Dasgupta · 2011
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Learning loss for active learning
Donggeun Yoo and In So Kweon · 2011
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Auto-encoding variational bayes
Diederik P. Kingma and M. Welling · 2014
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Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and Alexei A. Efros · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Tiny imagenet visual recognition challenge
Y. Le and X. Yang · 2015
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What’s the point: Semantic segmentation with point supervision
Amy L. Bearman, Olga Russakovsky, V. Ferrari, and Li Fei-Fei · 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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Dmytro Mishkin and Jiri Matas · 2016
Deep active learning for object detection
Soumya Roy, Asim Unmesh, and V. P. Namboodiri · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and S. Belongie · 2019
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Practical obstacles to deploying active learning
David Lowell, Zachary Chase Lipton, and Byron C. Wallace · 2019
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Parting with illusions about deep active learning
Sudhanshu Mittal, Maxim Tatarchenko, Özgün Çiçek, and Thomas Brox · 2019
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Cited alongside, same era.
Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
Cited alongside, same era.
Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krähenbühl, J. Donahue, Trevor Darrell, and Alexei A. Efros · 2016
Cited alongside, same era.
Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
Cited alongside, same era.
Representation learning by learning to count
M. Noroozi, H. Pirsiavash, and P. Favaro · 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.
Deep clustering for unsupervised learning of visual features
M. Caron, P. Bojanowski, Armand Joulin, and M. Douze · 2018
Cited alongside, same era.
Ali Mottaghi and Serena Yeung · 2019
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Rethinking deep active learning: Using unlabeled data at model training
Oriane Siméoni, Mateusz Budnik, Yannis Avrithis, and G. Gravier · 2019
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Variational adversarial active learning
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altch’e, C. Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, B. A. Pires, Zhaohan Daniel Guo, M. G. Azar, Bilal Piot, K. Kavukcuoglu, R. Munos, and Michal Valko · 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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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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