Under CopyrightRosenzweig, JuliaJuliaRosenzweigBrito, EduardoEduardoBritoKobialka, Hans-UlrichHans-UlrichKobialkaAkila, MaramMaramAkilaSchmidt, Nico M.Nico M.SchmidtSchlicht, PeterPeterSchlichtSchneider, Jan DavidJan DavidSchneiderHüger, FabianFabianHügerRottmann, MatthiasMatthiasRottmannHouben, SebastianSebastianHoubenWirtz, TimTimWirtz2022-03-1523.12.20212021https://publica.fraunhofer.de/handle/publica/41335110.24406/publica-fhg-413351Many machine learning applications can benefit from simulated data for systematic validation - in particular if real-life data is difficult to obtain or annotate. However, since simulations are prone to domain shift w.r.t. real-life data, it is crucial to verify the transferability of the obtained results. We propose a novel framework consisting of a generative label-to-image synthesis model together with different transferability measures to inspect to what extent we can transfer testing results of semantic segmentation models from synthetic data to equivalent real-life data. With slight modifications, our approach is extendable to, e.g., general multi-class classification tasks. Grounded on the transferability analysis, our approach additionally allows for extensive testing by incorporat ing controlled simulations. We validate our approach empirically on a semantic segmentation task on driving scenes. Transferability is tested using correlation analysis of IoU and a learned discriminator. Although the latter can distinguish between real-life and synthetic tests, in the former we observe surprisingly strong correlations of 0.7 for both cars and pedestrians.enmachine learningsimulation-based testingautomated driving005006629presentation