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Today's success of state of the art methods for semantic segmentation is driven by large datasets.
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Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)
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He, Y., Chiu, W.C., Keuper, M., Fritz, M.: Std2p: Rgbd semantic segmentation using spatio-temporal data-driven pooling. In: CVPR (2017)
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Lin, G., Milan, A., Shen, C., Reid, I.: Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. In: CVPR (2017)
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2017
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Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR (2017)
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Neuhold, G., Ollmann, T., Rota Bulo, S., Kontschieder, P.: The mapillary vistas dataset for semantic understanding of street scenes. In: ICCV (2017)
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Oh, S.J., Fritz, M., Schiele, B.: Adversarial image perturbation for privacy protection a game theory perspective. In: ICCV (2017)
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Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: IEEE Symposium on Security and Privacy (SP) (2017)
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Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: ICCV (2017)
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Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., Yuille, A.: Adversarial examples for semantic segmentation and object detection. In: ICCV (2017)
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Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR (2017)
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Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified perceptual parsing for scene understanding. In: ECCV (2018)
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Jayaraman, B., Evans, D.: Evaluating differentially private machine learning in practice. In: 28th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 19). pp. 1895–1912 (2019)
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Orekondy, T., Schiele, B., Fritz, M.: Knockoff nets: Stealing functionality of black-box models. In: CVPR (2019)
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Sablayrolles, A., Douze, M., Schmid, C., Ollivier, Y., Jegou, H.: White-box vs black-box: Bayes optimal strategies for membership inference. In: ICML (2019)
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Salem, A., Zhang, Y., Humbert, M., Fritz, M., Backes, M.: Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models. In: NDSS (2019)
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Stutz, D., Hein, M., Schiele, B.: Disentangling adversarial robustness and generalization. In: CVPR (2019)
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