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We investigate in this paper the architecture of deep convolutional networks.
Neural network ensembles
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Striving for simplicity: The all convolutional net
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S. E., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015) · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016a) · 2016
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Deep Networks with Stochastic Depth
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Deep learning without poor local minima
Kawaguchi, K. (2016) · 2016
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Wide residual networks
Zagoruyko, S. and Komodakis, N. (2016) · 2016
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Xception: Deep learning with depthwise separable convolutions
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Gastaldi, X. (2017) · 2017
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Densely connected convolutional networks
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SGDR: stochastic gradient descent with restarts
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Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K. (2017) · 2017
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A PyTorch Implementation of DenseNet
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Identity Mappings in Deep Residual Networks
He, K., Zhang, X., Ren, S., and Sun, J. (2016b)
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Neural architecture search with reinforcement learning
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Learning transferable architectures for scalable image recognition
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