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In this paper, for the first time, we propose an evaluation method for deep learning models that assesses the performance of a model not only in an unseen test scenario, but also in extreme cases of noise, outliers and ambiguous input data.
D.S. Marcus, T.H. Wang, J. Parker, J.G. Csernansky, J.C. Morris, R.L. Buckner: Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults. J. Cognitive Neuroscience 19(9): 1498–1507 2007
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C. Xie, J. Wang, Z. Zhang, Y. Zhou, L. Xie, A. L. Yuille: Adversarial Examples for Semantic Segmentation and Object Detection. ICCV 2017
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V. Badrinarayanan, A. Kendall, R. Cipolla: SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12): 2481–2495 2017
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S. Jégou, M. Drozdzal, D. Vázquez, A. Romero, Y. Bengio: The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. CVPR Workshops 2017
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N. Papernot, P. D. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, A. Swami: Practical Black-Box Attacks against Machine Learning. AsiaCCS 2017
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