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Quantifying the predictive uncertainty emerged as a possible solution to common challenges like overconfidence or lack of explainability and robustness of deep neural networks, albeit one that is often computationally expensive.
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Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
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FitNets: Hints for Thin Deep Nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2015
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Towards unified depth and semantic prediction from a single image
Peng Wang, Xiaohui Shen, Zhe Lin, Scott Cohen, Brian Price, and Alan L Yuille · 2015
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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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Uncertainty in deep learning
Yarin Gal · 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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Joint semantic segmentation and depth estimation with deep convolutional networks
Arsalan Mousavian, Hamed Pirsiavash, and Jana Košecká · 2016
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Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 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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Leveraging uncertainty information from deep neural networks for disease detection
Christian Leibig, Vaneeda Allken, Murat Seçkin Ayhan, Philipp Berens, and Siegfried Wahl · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning
Rowan McAllister, Yarin Gal, Alex Kendall, Mark van der Wilk, Amar Shah, Roberto Cipolla, and Adrian Weller · 2017
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Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, et al · 2017
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Driving scene perception network: Real-time joint detection, depth estimation and semantic segmentation
Liangfu Chen, Zeng Yang, Jianjun Ma, and Zheng Luo · 2018
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Dropout distillation for efficiently estimating model confidence
Corina Gurau, Alex Bewley, and Ingmar Posner · 2018
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Rgb-d semantic segmentation: a review
Yaosi Hu, Zhenzhong Chen, and Weiyao Lin · 2018
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Look deeper into depth: Monocular depth estimation with semantic booster and attention-driven loss
Jianbo Jiao, Ying Cao, Yibing Song, and Rynson Lau · 2018
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2018
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Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin · 2018
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Exploring relational context for multi-task dense prediction
David Brüggemann, Menelaos Kanakis, Anton Obukhov, Stamatios Georgoulis, and Luc Van Gool · 2021
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Iterative distillation for better uncertainty estimates in multitask emotion recognition
Didan Deng, Liang Wu, and Bertram E. Shi · 2021
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Sosd-net: Joint semantic object segmentation and depth estimation from monocular images
Lei He, Jiwen Lu, Guanghui Wang, Shiyu Song, and Jie Zhou · 2021
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Efficient uncertainty estimation in semantic segmentation via distillation
Christopher J. Holder and Muhammad Shafique · 2021
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Deep learning for monocular depth estimation: A review
Yue Ming, Xuyang Meng, Chunxiao Fan, and Hui Yu · 2021
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Real-time uncertainty estimation in computer vision via uncertainty-aware distribution distillation
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Collaborative deconvolutional neural networks for joint depth estimation and semantic segmentation
Jing Liu, Yuhang Wang, Yong Li, Jun Fu, Jiangyun Li, and Hanqing Lu · 2018
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Evaluating bayesian deep learning methods for semantic segmentation
Jishnu Mukhoti and Yarin Gal · 2018
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Machine Vision Algorithms and Applications
Carsten Steger, Markus Ulrich, and Christian Wiedemann · 2018
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Pad-net: Multi-tasks guided prediction-and-distillation network for simultaneous depth estimation and scene parsing
Dan Xu, Wanli Ouyang, Xiaogang Wang, and Nicu Sebe · 2018
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Depth estimation and semantic segmentation from a single rgb image using a hybrid convolutional neural network
Xiao Lin, Dalila Sánchez-Escobedo, Josep R Casas, and Montse Pardàs · 2019
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End-to-end multi-task learning with attention
Shikun Liu, Edward Johns, and Andrew J Davison · 2019
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Ensemble Distribution Distillation
Andrey Malinin, Bruno Mlodozeniec, and Mark Gales · 2019
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Yichen Shen, Zhilu Zhang, Mert R. Sabuncu, and Lin Sun · 2021
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A brief survey on rgb-d semantic segmentation using deep learning
Changshuo Wang, Chen Wang, Weijun Li, and Haining Wang · 2021
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Segformer: Simple and efficient design for semantic segmentation with transformers
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo · 2021
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A survey on multi-task learning
Yu Zhang and Qiang Yang · 2021
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Towards real-time monocular depth estimation for robotics: A survey
Xingshuai Dong, Matthew A Garratt, Sreenatha G Anavatti, and Hussein A Abbass · 2022
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Ci-net: A joint depth estimation and semantic segmentation network using contextual information
Tianxiao Gao, Wu Wei, Zhongbin Cai, Zhun Fan, Sheng Quan Xie, Xinmei Wang, and Qiuda Yu · 2022
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Predictive uncertainties for multi-task learning network
Tianxiao Gao, Wu Wei, Xinmei Wang, Qiuda Yu, and Zhun Fan · 2022
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A Survey of Uncertainty in Deep Neural Networks
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang, Richard Bamler, and Xiao Xiang Zhu · 2022
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Implementing machine learning: chances and challenges
Michael Heizmann, Alexander Braun, Markus Glitzner, Matthias Günther, Günther Hasna, Christina Klüver, Jakob Krooß, Erik Marquardt, Michael Overdick, and Markus Ulrich · 2022
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Image segmentation using deep learning: A survey
Shervin Minaee, Yuri Boykov, Fatih Porikli, Antonio Plaza, Nasser Kehtarnavaz, and Demetri Terzopoulos · 2022
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Learning structured gaussians to approximate deep ensembles
Ivor JA Simpson, Sara Vicente, and Neill DF Campbell · 2022
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Comparison of uncertainty quantification methods for CNN-based regression
Kira Wursthorn, Markus Hillemann, and Markus Ulrich · 2022
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Mtformer: Multi-task learning via transformer and cross-task reasoning
Xiaogang Xu, Hengshuang Zhao, Vibhav Vineet, Ser-Nam Lim, and Antonio Torralba · 2022
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Semantic segmentation and depth estimation based on residual attention mechanism
Naihua Ji, Huiqian Dong, Fanyun Meng, and Liping Pang · 2023
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Steven Landgraf, Markus Hillemann, Moritz Aberle, Valentin Jung, and Markus Ulrich · 2023
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U-ce: Uncertainty-aware cross-entropy for semantic segmentation
Steven Landgraf, Markus Hillemann, Kira Wursthorn, and Markus Ulrich · 2023
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Dudes: Deep uncertainty distillation using ensembles for semantic segmentation
Steven Landgraf, Kira Wursthorn, Markus Hillemann, and Markus Ulrich · 2023
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Deep deterministic uncertainty: A new simple baseline
Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip HS Torr, and Yarin Gal · 2023
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Sub-ensembles for fast uncertainty estimation in neural networks
Matias Valdenegro-Toro · 2023
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