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Overfitting in deep learning has been the focus of a number of recent works, yet its exact impact on the behavior of neural networks is not well understood.
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (ICLR) (2015)
2015
Earlier work this paper cites.
Landman, B.A., Xu, Z., Igelsias, J.E., Styner, M., Langerak, T.R., Klein, A.: 2015 miccai multi-atlas labeling beyond the cranial vault – workshop and challenge
2015
Earlier work this paper cites.
Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin softmax loss for convolutional neural networks. In: International Conference on Machine Leanring (ICML). pp. 507–516 (2016)
2016
Earlier work this paper cites.
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Sci. Data 4
2017
Cited alongside, same era.
Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Med. Image Anal. 36
2017
Cited alongside, same era.
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)
2017
Cited alongside, same era.
Valindria, V.V., Lavdas, I., Cerrolaza, J., Aboagye, E.O., Rockall, A.G., Rueckert, D., Glocker, B.: Small organ segmentation in whole-body mri using a two-stage fcn and weighting schemes. In: MICCAI-MLMI. pp. 346–354. Springer (2018)
2018
Later among the works it cites.
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. In: International Conference on Learning Representations (ICLR) (2018)
2018
Later among the works it cites.
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