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Convolutional neural networks (CNN) are capable of learning robust representation with different regularization methods and activations as convolutional layers are spatially correlated.
“Learning multiple layers of features from tiny images,”
Alex Krizhevsky, Geoffrey Hinton, et al., · 2009
Earlier work this paper cites.
“Imagenet classification with deep convolutional neural networks,”
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, · 2012
Earlier work this paper cites.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Earlier work this paper cites.
“Improved regularization of convolutional neural networks with cutout,”
Terrance DeVries and Graham W Taylor, · 2017
Earlier work this paper cites.
“Densely connected convolutional networks,”
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger, · 2017
Earlier work this paper cites.
“Unpaired image-to-image translation using cycle-consistent adversarial networks,”
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros, · 2017
Earlier work this paper cites.
“Residual attention network for image classification,”
Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, and Xiaoou Tang, · 2017
Cited alongside, same era.
“Dsod: Learning deeply supervised object detectors from scratch,”
Zhiqiang Shen, Zhuang Liu, Jianguo Li, Yu-Gang Jiang, Yurong Chen, and Xiangyang Xue, · 2017
Cited alongside, same era.
“Automatic differentiation in PyTorch,”
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer, · 2017
Cited alongside, same era.
“Dropblock: A regularization method for convolutional networks,”
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le, · 2018
Cited alongside, same era.
“mixup: Beyond empirical risk minimization,”
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz, · 2018
Cited alongside, same era.
“Multimodal unsupervised image-to-image translation,”
Xun Huang, Ming-Yu Liu, Serge Belongie, and Jan Kautz, · 2018
Later among the works it cites.
“Squeeze-and-excitation networks,”
Jie Hu, Li Shen, and Gang Sun, · 2018
Later among the works it cites.
“Cutmix: Regularization strategy to train strong classifiers with localizable features,”
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo, · 2019
Later among the works it cites.
“Efficientnet: Rethinking model scaling for convolutional neural networks,”
Mingxing Tan and Quoc Le, · 2019
Later among the works it cites.
“Improving object detection from scratch via gated feature reuse,”
Zhiqiang Shen, Honghui Shi, Jiahui Yu, Hai Phan, Rogerio Feris, Liangliang Cao, Ding Liu, Xinchao Wang, Thomas Huang, and Marios Savvides, · 2019
Later among the works it cites.
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