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In this paper I investigate the effect of random seed selection on the accuracy when using popular deep learning architectures for computer vision.
Analysis of dawnbench, a time-to-accuracy machine learning performance benchmark
Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Chris Ré, and Matei Zaharia · 2019
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Individual differences among deep neural network models
Johannes Mehrer, Courtney J Spoerer, Nikolaus Kriegeskorte, and Tim C Kietzmann · 2020
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Accounting for variance in machine learning benchmarks
Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Nazanin Mohammadi Sepahvand, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Tal Arbel, Chris Pal, Gael Varoquaux, and Pascal Vincent · 2021
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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