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This work proposes a semantic segmentation network that produces high-quality uncertainty estimates in a single forward pass.
A. Graves, “Practical variational inference for neural networks,” in Advances in Neural Information Processing Systems , 2011
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C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra, “Weight uncertainty in neural network,” in International Conference on Machine Learning , 2015
2015
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O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” International journal of computer vision , 2015
2015
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Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in International Conference on Machine Learning , 2016
2016
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M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016
2016
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C. Louizos and M. Welling, “Multiplicative normalizing flows for variational bayesian neural networks,” in International Conference on Machine Learning . PMLR, 2017
2017
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B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” Advances in neural information processing systems , 2017
2017
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A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” Advances in neural information processing systems , 2017
2017
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A. A. Alemi, I. Fischer, J. V. Dillon, and K. Murphy, “Deep variational information bottleneck,” in International Conference on Learning Representations , 2017
2017
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D. Hendrycks and K. Gimpel, “A baseline for detecting misclassified and out-of-distribution examples in neural networks,” Proceedings of International Conference on Learning Representations , 2017
2017
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , 2017
2017
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J. Gast and S. Roth, “Lightweight probabilistic deep networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018
2018
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D. Novotny, S. Albanie, D. Larlus, and A. Vedaldi, “Self-supervised learning of geometrically stable features through probabilistic introspection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018
2018
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T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida, “Spectral normalization for generative adversarial networks,” in International Conference on Learning Representations , 2018
2018
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2018
Cited alongside, same era.
K. Lee, K. Lee, H. Lee, and J. Shin, “A simple unified framework for detecting out-of-distribution samples and adversarial attacks,” in Advances in Neural Information Processing Systems , 2018
2018
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A. Malinin and M. Gales, “Predictive uncertainty estimation via prior networks,” Advances in neural information processing systems , 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2021
Later among the works it cites.
D. S. W. Williams, M. Gadd, D. D. Martini, and P. Newman, “Fool me once: Robust selective segmentation via out-of-distribution detection with contrastive learning,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
M. Assran, M. Caron, I. Misra, P. Bojanowski, A. Joulin, N. Ballas, and M. Rabbat, “Semi-supervised learning of visual features by non-parametrically predicting view assignments with support samples,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021
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2018
Cited alongside, same era.
O. Zendel, K. Honauer, M. Murschitz, D. Steininger, and G. F. Dominguez, “Wilddash - creating hazard-aware benchmarks,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018
2018
Cited alongside, same era.
R. Weston, S. H. Cen, P. Newman, and I. Posner, “Probably unknown: Deep inverse sensor modelling radar,” 2019 International Conference on Robotics and Automation (ICRA) , 2019
2019
Cited alongside, same era.
J. Postels, F. Ferroni, H. Coskun, N. Navab, and F. Tombari, “Sampling-free epistemic uncertainty estimation using approximated variance propagation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019
2019
Cited alongside, same era.
N. Tagasovska and D. Lopez-Paz, “Single-model uncertainties for deep learning,” Advances in Neural Information Processing Systems , 2019
2019
Cited alongside, same era.
J. R. van Amersfoort, L. Smith, Y. W. Teh, and Y. Gal, “Simple and scalable epistemic uncertainty estimation using a single deep deterministic neural network,” in ICML , 2020
2020
Cited alongside, same era.
J. Liu, Z. Lin, S. Padhy, D. Tran, T. Bedrax Weiss, and B. Lakshminarayanan, “Simple and principled uncertainty estimation with deterministic deep learning via distance awareness,” in Advances in Neural Information Processing Systems , 2020
2020
Cited alongside, same era.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning . PMLR, 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in Proceedings of the International Conference on Computer Vision (ICCV) , 2021
2021
Later among the works it cites.
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jegou, “Training data-efficient image transformers &; distillation through attention,” in International Conference on Machine Learning , 2021
2021
Later among the works it cites.
H. Wang, Z. Li, L. Feng, and W. Zhang, “Vim: Out-of-distribution with virtual-logit matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022
2022
Later among the works it cites.
J. Zhou, C. Wei, H. Wang, W. Shen, C. Xie, A. Yuille, and T. Kong, “ibot: Image bert pre-training with online tokenizer,” International Conference on Learning Representations (ICLR) , 2022
2022
Later among the works it cites.
G. Li, H. Zheng, D. Liu, C. Wang, B. Su, and C. Zheng, “Semmae: Semantic-guided masking for learning masked autoencoders,” Advances in Neural Information Processing Systems , 2022
2022
Later among the works it cites.
Y. Shi, N. Siddharth, P. Torr, and A. R. Kosiorek, “Adversarial masking for self-supervised learning,” in International Conference on Machine Learning , 2022
2022
Later among the works it cites.
B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar, “Masked-attention mask transformer for universal image segmentation,” 2022
2022
Later among the works it cites.
2023
Later among the works it cites.
D. Williams, D. De Martini, M. Gadd, and P. Newman, “Mitigating distributional shift in semantic segmentation via uncertainty estimation from unlabelled data,” in IEEE Transactions on Robotics (T-RO) , 2024
2024
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