To improve the transferability of the trained models, particularly in setups where only the healthy data class is shared between the two domains, we propose a new framework based on a Wasserstein GAN for Partial and OpenSet&Partial domain adaptation.
In the first step, we identify outliers (including the mislabeled samples) based on the update in the hypothesis space.
A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions.
no code implementations • 13 Jul 2018 • Thilo Stadelmann, Mohammadreza Amirian, Ismail Arabaci, Marek Arnold, Gilbert François Duivesteijn, Ismail Elezi, Melanie Geiger, Stefan Lörwald, Benjamin Bruno Meier, Katharina Rombach, Lukas Tuggener
Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks.