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A General Underwater Object Detector (GUOD) should perform well on most of underwater circumstances.
“Principles of risk minimization for learning theory,”
Vladimir Vapnik, · 1992
Earlier work this paper cites.
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Yaroslav Ganin and Victor Lempitsky, · 2015
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“Ssd: Single shot multibox detector,”
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg, · 2016
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“Image style transfer using convolutional neural networks,”
Leon A Gatys, Alexander S Ecker, and Matthias Bethge, · 2016
Earlier work this paper cites.
“Cut, paste and learn: Surprisingly easy synthesis for instance detection,”
Debidatta Dwibedi, Ishan Misra, and Martial Hebert, · 2017
Earlier work this paper cites.
“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
Earlier work this paper cites.
“Smoothgrad: removing noise by adding noise,”
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“mixup: Beyond empirical risk minimization,”
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz, · 2018
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel, · 2018
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“Yolov3: An incremental improvement,”
Joseph Redmon and Ali Farhadi, · 2018
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Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool, · 2018
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“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
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“Data augmentation for object detection via progressive and selective instance-switching,”
Wang Hao, Wang Qilong, Yang Fan, Zhang Weiqi, and Zuo Wangmeng, · 2019
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“Domain adaptation for object detection via style consistency,”
Adrian Lopez Rodriguez and Krystian Mikolajczyk, · 2019
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“Domain generalization by solving jigsaw puzzles,”
Fabio M Carlucci, Antonio D’Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi, · 2019
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“Episodic training for domain generalization,”
Da Li, Jianshu Zhang, Yongxin Yang, Cong Liu, Yi-Zhe Song, and Timothy M Hospedales, · 2019
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“Domain generalization with adversarial feature learning,”
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot, · 2018
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“Faster r-cnn for marine organisms detection and recognition using data augmentation,”
Hai Huang, Hao Zhou, Xu Yang, Lu Zhang, Lu Qi, and Ai-Yun Zang, · 2019
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“Photorealistic style transfer via wavelet transforms,”
Jaejun Yoo, Youngjung Uh, Sanghyuk Chun, Byeongkyu Kang, and Jung-Woo Ha, · 2019
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“Invariant risk minimization,”
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz, · 2019
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