16 citations · 17 across the 4 of their papers we have counts for
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cs.LG2020
Generative models with kernel distance in data space
Szymon Knop, Marcin Mazur, Przemysław Spurek +2
Generative models dealing with modeling a~joint data distribution are generally either autoencoder or GAN based. Both have their pros and cons, generating blurry images or being un…
eess.IV2020★ 1 cited
LocoGAN -- Locally Convolutional GAN
Łukasz Struski, Szymon Knop, Jacek Tabor +2
In the paper we construct a fully convolutional GAN model: LocoGAN, which latent space is given by noise-like images of possibly different resolutions. The learning is local, i.e.…