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Anytime inference requires a model to make a progression of predictions which might be halted at any time.
An analysis of time-dependent planning
Thomas L Dean and Mark S Boddy · 1988
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Using anytime algorithms in intelligent systems
Shlomo Zilberstein · 1996
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As time goes by—anytime semantic segmentation with iterative context forests
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Alex Grubb and Drew Bagnell · 2012
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Mykhaylo Andriluka, Leonid Pishchulin, Peter Gehler, and Bernt Schiele · 2014
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cuDNN: Efficient primitives for deep learning
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer · 2014
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Anytime recognition of objects and scenes
Sergey Karayev, Mario Fritz, and Trevor Darrell · 2014
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The role of context for object detection and semantic segmentation in the wild
Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille · 2014
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Learning both weights and connections for efficient neural network
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Distilling the knowledge in a neural network
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Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Impatient dnns-deep neural networks with dynamic time budgets
Manuel Amthor, Erik Rodner, and Joachim Denzler · 2016
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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
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Learning dynamic hierarchical models for anytime scene labeling
Buyu Liu and Xuming He · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Submanifold sparse convolutional networks
Benjamin Graham and Laurens van der Maaten · 2017
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4d spatio-temporal convnets: Minkowski convolutional neural networks
Christopher Choy, JunYoung Gwak, and Silvio Savarese · 2019
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Code for deep high-resolution representation learning for human pose estimation
HRNet Human Pose Estimation · 2019
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Code for deep high-resolution representation learning for visual recognition
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Learning anytime predictions in neural networks via adaptive loss balancing
Hanzhang Hu, Debadeepta Dey, Martial Hebert, and J Andrew Bagnell · 2019
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Rethinking the value of network pruning
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Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, and Kilian Q Weinberger · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
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Not all pixels are equal: Difficulty-aware semantic segmentation via deep layer cascade
Xiaoxiao Li, Ziwei Liu, Ping Luo, Chen Change Loy, and Xiaoou Tang · 2017
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Predictivenet: An energy-efficient convolutional neural network via zero prediction
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Dynamic deep neural networks: Optimizing accuracy-efficiency trade-offs by selective execution
Lanlan Liu and Jia Deng · 2017
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Deciding how to decide: Dynamic routing in artificial neural networks
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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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Pytorch: An imperative style, high-performance deep learning library
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Gil Shomron, Ron Banner, Moran Shkolnik, and Uri Weiser · 2019
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Deep high-resolution representation learning for human pose estimation
Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang · 2019
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Anytime stereo image depth estimation on mobile devices
Yan Wang, Zihang Lai, Gao Huang, Brian H Wang, Laurens Van Der Maaten, Mark Campbell, and Kilian Q Weinberger · 2019
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Multiscale deep equilibrium models
Shaojie Bai, Vladlen Koltun, and J Zico Kolter · 2020
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Fast sparse convnets
Erich Elsen, Marat Dukhan, Trevor Gale, and Karen Simonyan · 2020
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Pointrend: Image segmentation as rendering
Alexander Kirillov, Yuxin Wu, Kaiming He, and Ross Girshick · 2020
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Dynamic convolutions: Exploiting spatial sparsity for faster inference
Thomas Verelst and Tinne Tuytelaars · 2020
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Deep high-resolution representation learning for visual recognition
Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, et al · 2020
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Spatially adaptive inference with stochastic feature sampling and interpolation
Zhenda Xie, Zheng Zhang, Xizhou Zhu, Gao Huang, and Stephen Lin · 2020
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The hardware lottery
Sara Hooker · 2021
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