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Currently, the neural network architecture design is mostly guided by the \emph{indirect} metric of computation complexity, i.e., FLOPs.
Accelerating very deep convolutional networks for classification and detection
Zhang, X., Zou, J., He, K., Sun, J.: · 1955
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Efficient and accurate approximations of nonlinear convolutional networks
Zhang, X., Zou, J., Ming, X., He, K., Sun, J.: · 1992
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Speeding up convolutional neural networks with low rank expansions
Jaderberg, M., Vedaldi, A., Zisserman, A.: · 2014
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cudnn: Efficient primitives for deep learning
Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J., Catanzaro, B., Shelhamer, E.: · 2014
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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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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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., et al.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Xception: Deep learning with depthwise separable convolutions
Chollet, F.: · 2016
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Distributed deep learning using synchronous stochastic gradient descent
Das, D., Avancha, S., Mudigere, D., Vaidynathan, K., Sridharan, S., Kalamkar, D., Kaul, B., Dubey, P.: · 2016
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Deep roots: Improving cnn efficiency with hierarchical filter groups
Ioannou, Y., Robertson, D., Cipolla, R., Criminisi, A.: · 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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Neural architecture search with reinforcement learning
Zoph, B., Le, Q.V.: · 2016
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Learning efficient convolutional networks through network slimming
Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: · 2017
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Channel pruning for accelerating very deep neural networks
He, Y., Zhang, X., Sun, J.: · 2017
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Interleaved group convolutions for deep neural networks
Zhang, T., Qi, G.J., Xiao, B., Wang, J.: · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: · 2017
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Light-head r-cnn: In defense of two-stage object detector
Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., Sun, J.: · 2017
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Densely connected convolutional networks
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Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: · 2017
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Squeeze-and-excitation networks
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Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: · 2017
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Progressive neural architecture search
Liu, C., Zoph, B., Shlens, J., Hua, W., Li, L.J., Fei-Fei, L., Yuille, A., Huang, J., Murphy, K.: · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: · 2017
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Zhang, X., Zhou, X., Lin, M., Sun, J.: · 2017
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Xie, L., Yuille, A.: · 2017
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Large-scale evolution of image classifiers
Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, Y.L., Le, Q., Kurakin, A.: · 2017
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Large kernel matters–improve semantic segmentation by global convolutional network
Peng, C., Zhang, X., Yu, G., Luo, G., Sun, J.: · 2017
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Regularized evolution for image classifier architecture search
Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: · 2018
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Igcv3: Interleaved low-rank group convolutions for efficient deep neural networks
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