no code implementations • 23 Jun 2023 • Jan Dubiński, Kamil Deja, Sandro Wenzel, Przemysław Rokita, Tomasz Trzciński
In particular, we examine the performance of variational autoencoders and generative adversarial networks, expanding the GAN architecture by an additional regularisation network and a simple, yet effective postprocessing step.
no code implementations • 22 Jun 2023 • Jan Dubiński, Antoni Kowalczuk, Stanisław Pawlak, Przemysław Rokita, Tomasz Trzciński, Paweł Morawiecki
In this paper, we examine whether it is possible to determine if a specific image was used in the training set, a problem known in the cybersecurity community and referred to as a membership inference attack.
no code implementations • 4 Jul 2022 • Stanisław Pawlak, Filip Szatkowski, Michał Bortkiewicz, Jan Dubiński, Tomasz Trzciński
We introduce a new method for internal replay that modulates the frequency of rehearsal based on the depth of the network.
no code implementations • 4 Jul 2022 • Jan Dubiński, Kamil Deja, Sandro Wenzel, Przemysław Rokita, Tomasz Trzciński
Especially prone to mode collapse are conditional GANs, which tend to ignore the input noise vector and focus on the conditional information.
1 code implementation • 11 Jun 2020 • Kamil Deja, Jan Dubiński, Piotr Nowak, Sandro Wenzel, Tomasz Trzciński
To address these shortcomings, we introduce a novel method dubbed end-to-end Sinkhorn Autoencoder, that leverages sinkhorn algorithm to explicitly align distribution of encoded real data examples and generated noise.