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To safely deploy autonomous vehicles, onboard perception systems must work reliably at high accuracy across a diverse set of environments and geographies.
Probability of error of some adaptive pattern-recognition machines
H Scudder · 1965
Earlier work this paper cites.
Iterative reclassification procedure for constructing an asymptotically optimal rule of allocation in discriminant analysis
Geoffrey J McLachlan · 1975
Earlier work this paper cites.
Semi-supervised self-training of object detection models
Chuck Rosenberg, Martial Hebert, and Henry Schneiderman · 2005
Earlier work this paper cites.
Stanley: The robot that won the darpa grand challenge
Sebastian Thrun, Mike Montemerlo, Hendrik Dahlkamp, David Stavens, Andrei Aron, James Diebel, Philip Fong, John Gale, Morgan Halpenny, Gabriel Hoffmann, et al · 2006
Earlier work this paper cites.
Convolutional deep belief networks on cifar-10
Alex Krizhevsky and Geoff Hinton · 2010
Earlier work this paper cites.
Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
Optimol: automatic online picture collection via incremental model learning
Li-Jia Li and Li Fei-Fei · 2010
Earlier work this paper cites.
Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network, 2015
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation
George Papandreou, Liang-Chieh Chen, Kevin P Murphy, and Alan L Yuille · 2015
Earlier work this paper cites.
Sun rgb-d: A rgb-d scene understanding benchmark suite
Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Earlier work this paper cites.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
Earlier work this paper cites.
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
Earlier work this paper cites.
Unsupervised pixel-level domain adaptation with generative adversarial networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan · 2017
Earlier work this paper cites.
Scannet: Richly-annotated 3d reconstructions of indoor scenes, 2017
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
Earlier work this paper cites.
Learning from simulated and unsupervised images through adversarial training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Joshua Susskind, Wenda Wang, and Russell Webb · 2017
Earlier work this paper cites.
Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
Earlier work this paper cites.
Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net
Wenjie Luo, Bin Yang, and Raquel Urtasun · 2018
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Data distillation: Towards omni-supervised learning
Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, and Kaiming He · 2018
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, 2018
Antti Tarvainen and Harri Valpola · 2018
Cited alongside, same era.
Second: Sparsely embedded convolutional detection
Yan Yan, Yuxing Mao, and Bo Li · 2018
Cited alongside, same era.
Hdnet: Exploiting HD maps for 3d object detection
Bin Yang, Ming Liang, and Raquel Urtasun · 2018
Cited alongside, same era.
Pixor: Real-time 3d object detection from point clouds
1st place solution for waymo open dataset challenge–3d detection and domain adaptation
Zhuangzhuang Ding, Yihan Hu, Runzhou Ge, Li Huang, Sijia Chen, Yu Wang, and Jie Liao · 2020
Later among the works it cites.
Afdet: Anchor free one stage 3d object detection
Runzhou Ge, Zhuangzhuang Ding, Yihan Hu, Yu Wang, Sijia Chen, Li Huang, and Yuan Li · 2020
Later among the works it cites.
Streaming object detection for 3-d point clouds
Wei Han, Zhengdong Zhang, Benjamin Caine, Brandon Yang, Christoph Sprunk, Ouais Alsharif, Jiquan Ngiam, Vijay Vasudevan, Jonathon Shlens, and Zhifeng Chen · 2020
Later among the works it cites.
Structure aware single-stage 3d object detection from point cloud
Chenhang He, Hui Zeng, Jianqiang Huang, Xian-Sheng Hua, and Lei Zhang · 2020
Later among the works it cites.
One thousand and one hours: Self-driving motion prediction dataset
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Bin Yang, Wenjie Luo, and Raquel Urtasun · 2018
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Voxelnet: End-to-end learning for point cloud based 3d object detection
Yin Zhou and Oncel Tuzel · 2018
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Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring
David Berthelot, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel · 2019
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Pointpillars: Fast encoders for object detection from point clouds
Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom · 2019
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Starnet: Targeted computation for object detection in point clouds
Jiquan Ngiam, Benjamin Caine, Wei Han, Brandon Yang, Yuning Chai, Pei Sun, Yin Zhou, Xi Yi, Ouais Alsharif, Patrick Nguyen, et al · 2019
Cited alongside, same era.
John Houston, Guido Zuidhof, Luca Bergamini, Yawei Ye, Ashesh Jain, Sammy Omari, Vladimir Iglovikov, and Peter Ondruska · 2020
Later among the works it cites.
Self-training for end-to-end speech recognition
Jacob Kahn, Ann Lee, and Awni Hannun · 2020
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Pointaugment: an auto-augmentation framework for point cloud classification
Ruihui Li, Xianzhi Li, Pheng-Ann Heng, and Chi-Wing Fu · 2020
Later among the works it cites.
Weakly supervised 3d object detection from lidar point cloud
Qinghao Meng, Wenguan Wang, Tianfei Zhou, Jianbing Shen, Luc Van Gool, and Dengxin Dai · 2020
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Improved noisy student training for automatic speech recognition
Daniel S Park, Yu Zhang, Ye Jia, Wei Han, Chung-Cheng Chiu, Bo Li, Yonghui Wu, and Quoc V Le · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
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A simple semi-supervised learning framework for object detection
Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li, Han Zhang, Chen-Yu Lee, and Tomas Pfister · 2020
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Scalability in perception for autonomous driving: Waymo open dataset
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al · 2020
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3dioumatch: Leveraging iou prediction for semi-supervised 3d object detection, 2020
He Wang, Yezhen Cong, Or Litany, Yue Gao, and Leonidas J. Guibas · 2020
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Train in germany, test in the usa: Making 3d object detectors generalize
Yan Wang, Xiangyu Chen, Yurong You, Li Erran Li, Bharath Hariharan, Mark Campbell, Kilian Q Weinberger, and Wei-Lun Chao · 2020
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Multi-frame to single-frame: Knowledge distillation for 3d object detection
Yue Wang, Alireza Fathi, Jiajun Wu, Thomas Funkhouser, and Justin Solomon · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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Sess: Self-ensembling semi-supervised 3d object detection, 2020
Na Zhao, Tat-Seng Chua, and Gim Hee Lee · 2020
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End-to-end multi-view fusion for 3d object detection in lidar point clouds
Yin Zhou, Pei Sun, Yu Zhang, Dragomir Anguelov, Jiyang Gao, Tom Ouyang, James Guo, Jiquan Ngiam, and Vijay Vasudevan · 2020
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Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D Cubuk, and Quoc V Le · 2020
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Auto4d: Learning to label 4d objects from sequential point clouds, 2021
Bin Yang, Min Bai, Ming Liang, Wenyuan Zeng, and Raquel Urtasun · 2021
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