Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain Adaptation
Mert Keser,Artem Savkin,Federico Tombari,Mert Keser,Artem Savkin,Federico Tombari
Synthetic data generation is an appealing approach to generate novel traffic scenarios in autonomous driving. However, deep learning perception algorithms trained solely on synthetic data encounter serious performance drops when they are tested on real data. Such performance drops are commonly attributed to the domain gap between real and synthetic data. Domain adaptation methods that have been ap...
