7 citations · 11 across the 3 of their papers we have counts for
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cs.LG2020★ 7 cited
On Data Augmentation and Adversarial Risk: An Empirical Analysis
Hamid Eghbal-zadeh, Khaled Koutini, Paul Primus +5
Data augmentation techniques have become standard practice in deep learning, as it has been shown to greatly improve the generalisation abilities of models. These techniques rely o…
cs.LG2018
Mixture Density Generative Adversarial Networks
Hamid Eghbal-zadeh, Werner Zellinger, Gerhard Widmer
Generative Adversarial Networks have surprising ability for generating sharp and realistic images, though they are known to suffer from the so-called mode collapse problem. In this…