Fetching the paper…
Reading the bibliography…
Convolutional layers are a major driving force behind the successes of deep learning.
“Learning multiple layers of features from tiny images,”
Alex Krizhevsky and Geoffrey Hinton, · 2009
Earlier work this paper cites.
Min Lin, Qiang Chen, and Shuicheng Yan, · 2013
Earlier work this paper cites.
“Microsoft coco: Common objects in context,”
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick, · 2014
Earlier work this paper cites.
“Going deeper with convolutions,”
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich, · 2015
Earlier work this paper cites.
“Imagenet large scale visual recognition challenge,”
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al., · 2015
Earlier work this paper cites.
“Faster r-cnn: Towards real-time object detection with region proposal networks,”
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, · 2015
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.
Sergey Zagoruyko and Nikos Komodakis, · 2016
Cited alongside, same era.
“Densely connected convolutional networks,”
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger, · 2017
Cited alongside, same era.
“Mobilenets: Efficient convolutional neural networks for mobile vision applications,”
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam, · 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.
“Aggregated residual transformations for deep neural networks,”
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He, · 2017
Cited alongside, same era.
“Shufflenet: An extremely efficient convolutional neural network for mobile devices,”
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun, · 2018
Later among the works it cites.
“Cbam: Convolutional block attention module,”
Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon, · 2018
Later among the works it cites.
“Hetconv: Heterogeneous kernel-based convolutions for deep cnns,”
Pravendra Singh, Vinay Kumar Verma, Piyush Rai, and Vinay P. Namboodiri, · 2019
Closest in time.
“Hetconv: Beyond homogeneous convolution kernels for deep cnns,”
Pravendra Singh, Vinay Kumar Verma, Piyush Rai, and Vinay P Namboodiri, · 2019
Closest in time.
“Accuracy booster: Performance boosting using feature map re-calibration,”
Pravendra Singh, Pratik Mazumder, and Vinay P Namboodiri, · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“A faster pytorch implementation of faster r-cnn,”
Jianwei Yang, Jiasen Lu, Dhruv Batra, and Devi Parikh, · 2017
Cited alongside, same era.
“Mobilenetv2: Inverted residuals and linear bottlenecks,”
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen, · 2018
Cited alongside, same era.
Pravendra Singh, Vinay Sameer Raja Kadi, Nikhil Verma, and Vinay P Namboodiri, · 2019
Closest in time.
“Cooperative initialization based deep neural network training,”
Pravendra Singh, Munender Varshney, and Vinay P Namboodiri, · 2020
Closest in time.