  Common domain shift problem formulations consider the integration of multiple
source domains, or the target domain during training. Regarding the
generalization of machine learning models between different car interiors, we
formulate the criterion of training in a single vehicle: without access to the
target distribution of the vehicle the model would be deployed to, neither with
access to multiple vehicles during training. We performed an investigation on
the SVIRO dataset for occupant classification on the rear bench and propose an
autoencoder based approach to improve the transferability. The autoencoder is
on par with commonly used classification models when trained from scratch and
sometimes out-performs models pre-trained on a large amount of data. Moreover,
the autoencoder can transform images from unknown vehicles into the vehicle it
was trained on. These results are corroborated by an evaluation on real
infrared images from two vehicle interiors.
