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In this work, we present a new network design paradigm.
Backpropagation applied to handwritten zip code recognition
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Imagenet: A large-scale hierarchical image database
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Imagenet classification with deep convolutional neural networks
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Deeply-supervised nets
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Very deep convolutional networks for large-scale image recognition
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Going deeper with convolutions
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Wide residual networks
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Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2017
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Improved regularization of convolutional neural networks with cutout
T. DeVries and G. W. Taylor · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
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Progressive neural architecture search
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
N. Ma, X. Zhang, H.-T. Zheng, and J. Sun · 2018
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Efficient neural architecture search via parameter sharing
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2018
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Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2018
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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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Fractalnet: Ultra-deep neural networks without residuals
G. Larsson, M. Maire, and G. Shakhnarovich · 2017
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Searching for activation functions
P. Ramachandran, B. Zoph, and Q. V. Le · 2017
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2017
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AutoAugment: Learning augmentation policies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2018
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
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Darts: Differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2019
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On network design spaces for visual recognition
I. Radosavovic, J. Johnson, S. Xie, W.-Y. Lo, and P. Dollár · 2019
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Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2019
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Do imagenet classifiers generalize to imagenet?
B. Recht, R. Roelofs, L. Schmidt, and V. Shankar · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
M. Tan and Q. V. Le · 2019
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