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With the goal of making deep learning more label-efficient, a growing number of papers have been studying active learning (AL) for deep models.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
k-means++: the advantages of careful seeding
David Arthur and Sergei Vassilvitskii · 2007
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 learning literature survey
Burr Settles · 2009
Earlier work this paper cites.
Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Ng · 2011
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Submodularity in data subset selection and active learning
Kai Wei, Rishabh Iyer, and Jeff Bilmes · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Deepfool: A simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Earlier work this paper cites.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Xavier Gastaldi · 2017
Cited alongside, same era.
The effectiveness of data augmentation in image classification using deep learning
Luis Perez and Jason Wang · 2017
Cited alongside, same era.
The marginal value of adaptive gradient methods in machine learning
Ashia C Wilson, Rebecca Roelofs, Mitchell Stern, Nathan Srebro, and Benjamin Recht · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
Learning from less data: A unified data subset selection and active learning framework for computer vision
Vishal Kaushal, Rishabh Iyer, Suraj Kothawade, Rohan Mahadev, Khoshrav Doctor, and Ganesh Ramakrishnan · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 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.
On warm-starting neural network training
Jordan Ash and Ryan P Adams · 2020
Later among the works it cites.
Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
Later among the works it cites.
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Cited alongside, same era.
Adversarial active learning for deep networks: a margin based approach
Melanie Ducoffe and Frederic Precioso · 2018
Cited alongside, same era.
Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 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.
Deep layer aggregation
Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell · 2018
Cited alongside, same era.
Deep active learning with badge
Jordan Ash · 2019
Cited alongside, same era.
Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal · 2019
Cited alongside, same era.
Deep active learning
Kuan-Hao Huang · 2019
Cited alongside, same era.
Prateek Munjal, Nasir Hayat, Munawar Hayat, Jamshid Sourati, and Shadab Khan · 2020
Later among the works it cites.
apricot: Submodular selection for data summarization in python
Jacob M Schreiber, Jeffrey A Bilmes, and William Stafford Noble · 2020
Later among the works it cites.
Towards theoretically understanding why sgd generalizes better than adam in deep learning
Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven HOI, and Weinan E · 2020
Later among the works it cites.
Submodular combinatorial information measures with applications in machine learning
Rishabh Iyer, Ninad Khargoankar, Jeff Bilmes, and Himanshu Asanani · 2021
Closest in time.
Grad-match: A gradient matching based data subset selection for efficient learning
Krishnateja Killamsetty, Durga Sivasubramanian, Baharan Mirzasoleiman, Ganesh Ramakrishnan, Abir De, and Rishabh Iyer · 2021
Closest in time.
Glister: A generalization based data selection framework for efficient and robust learning
Krishnateja Killamsetty, Durga Subramanian, Ganesh Ramakrishnan, and Rishabh Iyer · 2021
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