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The goal of pool-based active learning is to judiciously select a fixed-sized subset of unlabeled samples from a pool to query an oracle for their labels, in order to maximize the accuracy of a supervised learner.
Queries and concept learning
Dana Angluin · 1988
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Improving generalization with active learning
L. Atlas D. Cohn and R. Ladner · 1994
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Support vector machine active learning with applications to text classification
Simon Tong and Daphne Koller · 2002
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
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Query by committee made real
Ran Gilad-bachrach, Amir Navot, and Naftali Tishby · 2006
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A notion of task relatedness yielding provable multiple-task learning guarantees
Shai Ben-David and Reba Schuller Borbely · 2008
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A general agnostic active learning algorithm
Sanjoy Dasgupta, Daniel J Hsu, and Claire Monteleoni · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky · 2009
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Active learning literature survey
Burr Settles · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Multi-task learning in deep neural networks for improved phoneme recognition
Michael L Seltzer and Jasha Droppo · 2013
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Facial landmark detection by deep multi-task learning
Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaoou Tang · 2014
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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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
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks, 2016
Dan Hendrycks and Kevin Gimpel · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, and Alexei Efros · 2016
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Accelerating very deep convolutional networks for classification and detection
X. Zhang, J. Zou, K. He, and J. Sun · 2016
Generative adversarial active learning
Jia-Jie Zhu and José Bento · 2017
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The power of ensembles for active learning in image classification
William H. Beluch, Tim Genewein, Andreas Nürnberger, and Jan M. Köhler · 2018
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Adversarial active learning for deep networks: a margin based approach
Melanie Ducoffe and Frédéric Precioso · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Deep anomaly detection using geometric transformations
Izhak Golan and Ran El-Yaniv · 2018
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Identifying beneficial task relations for multi-task learning in deep neural networks
Joachim Bingel and Anders Søgaard · 2017
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Rethinking atrous convolution for semantic image segmentation, 2017
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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Multi-task self-supervised visual learning, 2017
Carl Doersch and Andrew Zisserman · 2017
Cited alongside, same era.
Deep bayesian active learning with image data, 2017
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
Cited alongside, same era.
Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
Cited alongside, same era.
Active decision boundary annotation with deep generative models
M. Huijser and J. C. V. Gemert · 2017
Cited alongside, same era.
Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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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
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Using self-supervised learning can improve model robustness and uncertainty, 2019
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
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Mobilenetv2: Inverted residuals and linear bottlenecks, 2019
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2019
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Variational adversarial active learning
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell · 2019
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Learning loss for active learning
Donggeun Yoo and In So Kweon · 2019
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A simple framework for contrastive learning of visual representations, 2020
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Momentum contrast for unsupervised visual representation learning, 2020
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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