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For all the ways convolutional neural nets have revolutionized computer vision in recent years, one important aspect has received surprisingly little attention: the effect of image size on the accuracy of tasks being trained for.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Deconvolutional networks
Matthew D Zeiler, Dilip Krishnan, Graham W Taylor, and Rob Fergus · 2010
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Systematic evaluation of super-resolution using classification
Vinay P Namboodiri, Vincent De Smet, and Luc Van Gool · 2011
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Ava: A large-scale database for aesthetic visual analysis
Naila Murray, Luca Marchesotti, and Florent Perronnin · 2012
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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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
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Training and investigating residual nets
Sam Gross and Michael Wilber · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Xiao-Jiao Mao, Chunhua Shen, and Yu-Bin Yang · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Dirty pixels: Optimizing image classification architectures for raw sensor data
Steven Diamond, Vincent Sitzmann, Stephen Boyd, Gordon Wetzstein, and Felix Heide · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
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Aod-net: All-in-one dehazing network
Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, and Dan Feng · 2017
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Enhanced deep residual networks for single image super-resolution
Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee · 2017
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When image denoising meets high-level vision tasks: A deep learning approach
Ding Liu, Bihan Wen, Xianming Liu, Zhangyang Wang, and Thomas S Huang · 2017
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Super-resolution imaging
Peyman Milanfar · 2017
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Quality-adaptive deep learning for pedestrian detection
Khalid Tahboub, David Güera, Amy R Reibman, and Edward J Delp · 2017
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Enhancing the performance of convolutional neural networks on quality degraded datasets
Super-identity convolutional neural network for face hallucination
Kaipeng Zhang, Zhanpeng Zhang, Chia-Wen Cheng, Winston H Hsu, Yu Qiao, Wei Liu, and Tong Zhang · 2018
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Toward real-world single image super-resolution: A new benchmark and a new model
Jianrui Cai, Hui Zeng, Hongwei Yong, Zisheng Cao, and Lei Zhang · 2019
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Gpipe: Efficient training of giant neural networks using pipeline parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V Le, Yonghui Wu, et al · 2019
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Transferable recognition-aware image processing
Zhuang Liu, Tinghui Zhou, Hung-Ju Wang, Zhiqiang Shen, Bingyi Kang, Evan Shelhamer, and Trevor Darrell · 2019
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Dual directed capsule network for very low resolution image recognition
Maneet Singh, Shruti Nagpal, Richa Singh, and Mayank Vatsa · 2019
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Jonghwa Yim and Kyung-Ah Sohn · 2017
Cited alongside, same era.
Finding tiny faces in the wild with generative adversarial network
Yancheng Bai, Yongqiang Zhang, Mingli Ding, and Bernard Ghanem · 2018
Cited alongside, same era.
Task-driven super resolution: Object detection in low-resolution images
Muhammad Haris, Greg Shakhnarovich, and Norimichi Ukita · 2018
Cited alongside, same era.
End-to-end united video dehazing and detection
Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, and Dan Feng · 2018
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Disentangling features in 3d face shapes for joint face reconstruction and recognition
Feng Liu, Ronghang Zhu, Dan Zeng, Qijun Zhao, and Xiaoming Liu · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Classification-driven dynamic image enhancement
Vivek Sharma, Ali Diba, Davy Neven, Michael S Brown, Luc Van Gool, and Rainer Stiefelhagen · 2018
Cited alongside, same era.
Image pre-transformation for recognition-aware image compression
Satoshi Suzuki, Motohiro Takagi, Kazuya Hayase, Takayuki Onishi, and Atsushi Shimizu · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
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Fixing the train-test resolution discrepancy
Hugo Touvron, Andrea Vedaldi, Matthijs Douze, and Hervé Jégou · 2019
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Kernel modeling super-resolution on real low-resolution images
Ruofan Zhou and Sabine Susstrunk · 2019
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Creating high resolution images with a latent adversarial generator
David Berthelot, Peyman Milanfar, and Ian Goodfellow · 2020
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The rate-distortion-accuracy tradeoff: Jpeg case study
Xiyang Luo, Hossein Talebi, Feng Yang, Michael Elad, and Peyman Milanfar · 2020
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The effect of image resolution on deep learning in radiography
Carl F Sabottke and Bradley M Spieler · 2020
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Bridging the gap between computational photography and visual recognition
Walter Scheirer, Rosaura VidalMata, Sreya Banerjee, Brandon RichardWebster, Michael Albright, Pedro Davalos, Scott McCloskey, Ben Miller, Asongu Tambo, Sushobhan Ghosh, et al · 2020
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Better compression with deep pre-editing
Hossein Talebi, Damien Kelly, Xiyang Luo, Ignacio Garcia Dorado, Feng Yang, Peyman Milanfar, and Michael Elad · 2020
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Thumbnet: One thumbnail image contains all you need for recognition
Chen Zhao and Bernard Ghanem · 2020
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