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In this study, a novel self-supervised learning (SSL) method is proposed, which considers SSL in terms of variational inference to learn not only representation but also representation uncertainties.
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Diederik P Kingma and Max Welling · 2013
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Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Sergey Ioffe and Christian Szegedy · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Mehdi Noroozi and Paolo Favaro · 2016
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Jianxiong Xiao, Krista A Ehinger, James Hays, Antonio Torralba, and Aude Oliva · 2016
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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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Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Hyperspherical variational auto-encoders
Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak · 2018
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Uncertainty estimates and multi-hypotheses networks for optical flow
Eddy Ilg, Ozgun Cicek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, and Thomas Brox · 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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Nikos Komodakis and Spyros Gidaris · 2018
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Ilia Petrov, Vlad Shakhuro, and Anton Konushin · 2018
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Jakub Tomczak and Max Welling · 2018
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Mike Wu and Noah Goodman · 2018
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Zhirong Wu, Yuanjun Xiong, X Yu Stella, and Dahua Lin · 2018
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Masashi Okada, Shinji Takenaka, and Tadahiro Taniguchi · 2020
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Self-supervised bayesian deep learning for image recovery with applications to compressive sensing
Tongyao Pang, Yuhui Quan, and Hui Ji · 2020
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On the uncertainty of self-supervised monocular depth estimation
Matteo Poggi, Filippo Aleotti, Fabio Tosi, and Stefano Mattoccia · 2020
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Uncertainty-aware self-supervised 3d data association
Jianren Wang, Siddharth Ancha, Yi-Ting Chen, and David Held · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Nitesh B Gundavarapu, Divyansh Srivastava, Rahul Mitra, Abhishek Sharma, and Arjun Jain · 2019
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Bounding box regression with uncertainty for accurate object detection
Yihui He, Chenchen Zhu, Jianren Wang, Marios Savvides, and Xiangyu Zhang · 2019
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Multi-source neural variational inference
Richard Kurle, Stephan Günnemann, and Patrick Van der Smagt · 2019
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Safe reinforcement learning with model uncertainty estimates
Björn Lütjens, Michael Everett, and Jonathan P How · 2019
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Variational mixture-of-experts autoencoders for multi-modal deep generative models
Yuge Shi, Brooks Paige, Philip Torr, et al · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
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Safety enhancement for deep reinforcement learning in autonomous separation assurance
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Self-supervised learning: Generative or contrastive
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von mises-fisher loss: An exploration of embedding geometries for supervised learning
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Generalized multimodal ELBO
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Understanding self-supervised learning dynamics without contrastive pairs
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Barlow twins: Self-supervised learning via redundancy reduction
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Predictor networks and stop-grads provide implicit variance regularization in byol/simsiam
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