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Learning-based approaches for semantic segmentation have two inherent challenges.
A mathematical theory of communication
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Earlier work this paper cites.
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Q-learning
Christopher JCH Watkins and Peter Dayan · 1992
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Information, Prediction, and Query by Committee
Yoav Freund, H Sebastian Seung, Eli Shamir, and Naftali Tishby · 1993
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Committee-based sampling for training probabilistic classifiers
Ido Dagan and Sean P. Engelson · 1995
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Feature pyramid networks for object detection
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Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations
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Cost-effective active learning for deep image classification
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Suggestive annotation: A deep active learning framework for biomedical image segmentation
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Rgb-d object detection and semantic segmentation for autonomous manipulation in clutter
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Active learning for convolutional neural networks: A core-set approach
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Application of decision rules for handling class imbalance in semantic segmentation
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