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Self-supervised learning (SSL) is a technique for learning useful representations from unlabeled data.
ADAM: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
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Shapenet: An information-rich 3D model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Discriminative unsupervised feature learning with exemplar convolutional neural networks
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2015
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Voxnet: A 3D convolutional neural network for real-time object recognition
Daniel Maturana and Sebastian Scherer · 2015
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Unsupervised learning of visual representations using videos
Xiaolong Wang and Abhinav Gupta · 2015
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3D shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Deep reconstruction-classification networks for unsupervised domain adaptation
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, and Wen Li · 2016
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Shuffle and learn: unsupervised learning using temporal order verification
Ishan Misra, C Lawrence Zitnick, and Martial Hebert · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
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Volumetric and multi-view CNNs for object classification on 3D data
Charles R Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas · 2016
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ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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Self-supervised video representation learning with odd-one-out networks
Basura Fernando, Hakan Bilen, Efstratios Gavves, and Stephen Gould · 2017
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Escape from cells: Deep kd-networks for the recognition of 3D point cloud models
Roman Klokov and Victor Lempitsky · 2017
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Convolutional neural networks on surfaces via seamless toric covers
Haggai Maron, Meirav Galun, Noam Aigerman, Miri Trope, Nadav Dym, Ersin Yumer, Vladimir G Kim, and Yaron Lipman · 2017
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Pointnet: Deep learning on point sets for 3D classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Learning representations and generative models for 3D point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
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Point convolutional neural networks by extension operators
Matan Atzmon, Haggai Maron, and Yaron Lipman · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Monte carlo convolution for learning on non-uniformly sampled point clouds
Pedro Hermosilla, Tobias Ritschel, Pere-Pau Vázquez, Àlvar Vinacua, and Timo Ropinski · 2018
PointDAN: A multi-scale 3D domain adaption network for point cloud representation
Can Qin, Haoxuan You, Lichen Wang, C-C Jay Kuo, and Yun Fu · 2019
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Cross-sensor deep domain adaptation for LiDAR detection and segmentation
Christoph B Rist, Markus Enzweiler, and Dariu M Gavrila · 2019
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Domain adaptation for vehicle detection from bird’s eye view LiDAR point cloud data
Khaled Saleh, Ahmed Abobakr, Mohammed Attia, Julie Iskander, Darius Nahavandi, Mohammed Hossny, and Saeid Nahvandi · 2019
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Self-supervised deep learning on point clouds by reconstructing space
Jonathan Sauder and Bjarne Sievers · 2019
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Unsupervised domain adaptation through self-supervision
Yu Sun, Eric Tzeng, Trevor Darrell, and Alexei A Efros · 2019
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Pointwise convolutional neural networks
Binh-Son Hua, Minh-Khoi Tran, and Sai-Kit Yeung · 2018
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PointCNN: Convolution on x-transformed points
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen · 2018
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Cross-domain self-supervised multi-task feature learning using synthetic imagery
Zhongzheng Ren and Yong Jae Lee · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Splatnet: Sparse lattice networks for point cloud processing
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, and Jan Kautz · 2018
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Learning to adapt structured output space for semantic segmentation
Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang, and Manmohan Chandraker · 2018
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Ali Thabet, Humam Alwassel, and Bernard Ghanem · 2019
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Dynamic graph CNN for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2019
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Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a LiDAR point cloud
Bichen Wu, Xuanyu Zhou, Sicheng Zhao, Xiangyu Yue, and Kurt Keutzer · 2019
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Self-supervised domain adaptation for computer vision tasks
Jiaolong Xu, Liang Xiao, and Antonio M López · 2019
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Adversarial domain adaptation with domain Mixup
Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang · 2019
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d-SNE: Domain adaptation using stochastic neighborhood embedding
Xiang Xu, Xiong Zhou, Ragav Venkatesan, Gurumurthy Swaminathan, and Orchid Majumder · 2019
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Unsupervised feature learning for point cloud by contrasting and clustering with graph convolutional neural network
Ling Zhang and Zhigang Zhu · 2019
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Joint supervised and self-supervised learning for 3D real-world challenges
Antonio Alliegro, Davide Boscaini, and Tatiana Tommasi · 2020
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Deep learning for 3D point clouds: A survey
Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun · 2020
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xMUDA: Cross-modal unsupervised domain adaptation for 3D semantic segmentation
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Universal domain adaptation through self supervision
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Adapting object detectors with conditional domain normalization
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Improving semantic analysis on point clouds via auxiliary supervision of local geometric priors
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Cascaded refinement network for point cloud completion
Xiaogang Wang, Marcelo H Ang Jr, and Gim Hee Lee · 2020
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Improve unsupervised domain adaptation with Mixup training
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Complete & label: A domain adaptation approach to semantic segmentation of LiDAR point clouds
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