  Deep Neural Networks (DNNs) are a critical component for self-driving
vehicles. They achieve impressive performance by reaping information from high
amounts of labeled data. Yet, the full complexity of the real world cannot be
encapsulated in the training data, no matter how big the dataset, and DNNs can
hardly generalize to unseen conditions. Robustness to various image
corruptions, caused by changing weather conditions or sensor degradation and
aging, is crucial for safety when such vehicles are deployed in the real world.
We address this problem through a novel type of layer, dubbed StyleLess, which
enables DNNs to learn robust and informative features that can cope with
varying external conditions. We propose multiple variations of this layer that
can be integrated in most of the architectures and trained jointly with the
main task. We validate our contribution on typical autonomous-driving tasks
(detection, semantic segmentation), showing that in most cases, this approach
improves predictive performance on unseen conditions (fog, rain), while
preserving performance on seen conditions and objects.
