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In this paper, we reveal the importance and benefits of introducing second-order operations into deep neural networks.
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80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
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ImageNet: A Large-Scale Hierarchical Image Database
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Rectified Linear Units Improve Restricted Boltzmann Machines
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Reading Digits in Natural Images with Unsupervised Feature Learning
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ImageNet Classification with Deep Convolutional Neural Networks
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Maxout Networks
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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
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Generative Adversarial Nets
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CAFFE: Convolutional Architecture for Fast Feature Embedding
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The Kaggle CIFAR10 Network
Nagadomi · 2014
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CNN Features off-the-shelf: an Astounding Baseline for Recognition
A. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
Going Deeper with Convolutions
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Image Classification and Retrieval are ONE
L. Xie, R. Hong, B. Zhang, and Q. Tian · 2015
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Holistically-Nested Edge Detection
S. Xie and Z. Tu · 2015
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Attention to Scale: Scale-Aware Semantic Image Segmentation
L. Chen, Y. Yang, J. Wang, W. Xu, and A. Yuille · 2016
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Compact Bilinear Pooling
Y. Gao, O. Beijbom, N. Zhang, and T. Darrell · 2016
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ResNet Training on Torch
S. Gross and M. Wilber · 2016
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Deep Residual Learning for Image Recognition
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. Yuille · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
S. Ioffe and C. Szegedy · 2015
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Spatial Transformer Networks
M. Jaderberg, K. Simonyan, and A. Zisserman · 2015
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Deeply-Supervised Nets
C. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu · 2015
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Recurrent Convolutional Neural Network for Object Recognition
M. Liang and X. Hu · 2015
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K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Identity Mappings in Deep Residual Networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Deep Networks with Stochastic Depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Weinberger · 2016
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Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree
C. Lee, P. Gallagher, and Z. Tu · 2016
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Factorized Bilinear Models for Image Recognition
Y. Li, N. Wang, J. Liu, and X. Hou · 2016
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J. Wang, Z. Wei, T. Zhang, and W. Zeng · 2016
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Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons
L. Xie, Q. Tian, J. Flynn, J. Wang, and A. Yuille · 2016
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InterActive: Inter-layer Activeness Propagation
L. Xie, L. Zheng, J. Wang, A. Yuille, and Q. Tian · 2016
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Residual Network Test
J. Xu · 2016
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Wide Residual Networks
S. Zagoruyko and N. Komodakis · 2016
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The Shattered Gradients Problem: If ResNets are the Answer, then What is the Question?
D. Balduzzi, M. Frean, L. Leary, J. Lewis, K. W.-D. Ma, and B. McWilliams · 2017
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Densely Connected Convolutional Networks
G. Huang, Z. Liu, K. Weinberger, and L. van der Maaten · 2017
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Aggregated Residual Transformations for Deep Neural Networks
S. Xie, R. Girshick, P. Dollar, Z. Tu, and K. He · 2017
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