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We investigate the optimization of neural networks on symmetric data, and compare the strategy of constraining the architecture to be equivariant to that of using data augmentation.
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Kanchana Vaishnavi Gandikota, Jonas Geiping, Zorah Lähner, Adam Czapliński, and Michael Moeller · 2021
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Philip Müller, Vladimir Golkov, Valentina Tomassini, and Daniel Cremers · 2021
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