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Active learning aims to reduce labeling costs by selecting only the most informative samples on a dataset.
Mixture density networks
Christopher M Bishop · 1994
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Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management
Stephen C. Hora · 1996
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A mathematical theory of communication
Claude E. Shannon · 2001
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The pascal visual object classes (VOC) challenge
Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, and Andrew Zisserman · 2010
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Active Learning
Burr Settles · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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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
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Fast r-cnn
Ross Girshick · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 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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Tyler Highlander and Andres Rodriguez · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 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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Large-Scale Visual Active Learning with Deep Probabilistic Ensembles
Gaussian yolov3: An accurate and fast object detector using localization uncertainty for autonomous driving
Jiwoong Choi, Dayoung Chun, Hyun Kim, and Hyuk-Jae Lee · 2019
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Deep active learning for efficient training of a lidar 3d object detector
Di Feng, Xiao Wei, Lars Rosenbaum, Atsuto Maki, and Klaus Dietmayer · 2019
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Deep multivariate mixture of gaussians for object detection under occlusion
Yihui He and Jianren Wang · 2019
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Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
Eyke Hüllermeier and Willem Waegeman · 2019
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Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Andreas Kirsch, Joost van Amersfoort, and Yarin Gal · 2019
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Kashyap Chitta, Jose M. Alvarez, and Adam Lesnikowski · 2018
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Uncertainty-aware learning from demonstration using mixture density networks with sampling-free variance modeling
Sungjoon Choi, Kyungjae Lee, Sungbin Lim, and Songhwai Oh · 2018
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Towards safe autonomous driving: Capture uncertainty in the deep neural network for lidar 3d vehicle detection
Di Feng, Lars Rosenbaum, and Klaus Dietmayer · 2018
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Localization-aware active learning for object detection
Chieh-Chi Kao, Teng-Yok Lee, Pradeep Sen, and Ming-Yu Liu · 2018
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Deep active learning for object detection
Soumya Roy, Asim Unmesh, and Vinay 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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Active learning for deep detection neural networks
Hamed Habibi Aghdam, Abel Gonzalez-Garcia, Antonio M. López, and Joost van de Weijer · 2019
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Single-model uncertainties for deep learning
Natasa Tagasovska and David Lopez-Paz · 2019
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Learning loss for active learning
Donggeun Yoo and In So Kweon · 2019
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Mixture-model-based bounding box density estimation for object detection
Jaeyoung Yoo, Geonseok Seo, and Nojun Kwak · 2019
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Task agnostic robust learning on corrupt outputs by correlation-guided mixture density networks
Sungjoon Choi, Sanghoon Hong, Kyungjae Lee, and Sungbin Lim · 2020
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Bayesod: A bayesian approach for uncertainty estimation in deep object detectors
Ali Harakeh, Michael Smart, and Steven L Waslander · 2020
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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
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Uncertainty quantification using bayesian neural networks in classification: Application to biomedical image segmentation
Yongchan Kwon, Joong-Ho Won, Beom Joon Kim, and Myunghee Cho Paik · 2020
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Mixture dense regression for object detection and human pose estimation
Ali Varamesh and Tinne Tuytelaars · 2020
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