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Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors.
Backpropagation applied to handwritten zip code recognition
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: · 1989
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Long short-term memory
Hochreiter, S., Schmidhuber, J.: · 1997
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Learning multiple layers of features from tiny images
Krizhevsky, A.: · 2009
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Rectified linear units improve restricted boltzmann machines
Nair, V., Hinton, G.E.: · 2010
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Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: · 2012
Earlier work this paper cites.
Microsoft COCO: Common objects in context
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
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Graham, B.: · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: · 2014
Earlier work this paper cites.
Network in network
Lin, M., Chen, Q., Yan, S.: · 2014
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: · 2015
Cited alongside, same era.
Highway networks
Srivastava, R.K., Greff, K., Schmidhuber, J.: · 2015
Cited alongside, same era.
Training very deep networks
Srivastava, R.K., Greff, K., Schmidhuber, J.: · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
Cited alongside, same era.
Deeply-supervised nets
Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Fast and accurate deep network learning by exponential linear units (ELUs)
Clevert, D.A., Unterthiner, T., Hochreiter, S.: · 2016
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All you need is a good init
Mishkin, D., Matas, J.: · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: · 2016
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Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: · 2015
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
Cited alongside, same era.
Szegedy, C., Ioffe, S., Vanhoucke, V.: · 2016
Closest in time.