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We present a method that "meta" classifies whether seg-ments predicted by a semantic segmentation neural networkintersect with the ground truth.
The distribution of the flora in the alpine zone
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Statistical validation of image segmentation quality based on a spatial overlap index
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The multimodal brain tumor image segmentation benchmark (brats)
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U-net: Convolutional networks for biomedical image segmentation
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2016
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Principled detection of out-of-distribution examples in neural networks
S. Liang, Y. Li, and R. Srikant · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam · 2018
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Efficient uncertainty estimation for semantic segmentation in videos
P.-Y. Huang, W.-T. Hsu, C.-Y. Chiu, T.-F. Wu, and M. Sun · 2018
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Deep convolutional neural networks using u-net for automatic brain tumor segmentation in multimodal mri volumes
A. Kermi, I. Mahmoudi, and M. T. Khadir · 2018
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Classification uncertainty of deep neural networks based on gradient information
P. Oberdiek, M. Rottmann, and H. Gottschalk · 2018
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