Sit Back and Relax: Learning to Drive Incrementally in All Weather
  Conditions
Stefan Leitner M. Jehanzeb Mirza Wei Lin Jakub Micorek Marc Masana Mateusz Kozinski Horst Possegger Horst Bischof
In autonomous driving scenarios, current object detection models show strong
performance when tested in clear weather. However, their performance
deteriorates significantly when tested in degrading weather conditions. In
addition, even when adapted to perform robustly in a sequence of different
weather conditions, they are often unable to perform well in all of them and
suffer from catastrophic forgetting. To efficiently mitigate forgetting, we
propose Domain-Incremental Learning through Activation Matching (DILAM), which
employs unsupervised feature alignment to adapt only the affine parameters of a
clear weather pre-trained network to different weather conditions. We propose
to store these affine parameters as a memory bank for each weather condition
and plug-in their weather-specific parameters during driving (i.e. test time)
when the respective weather conditions are encountered. Our memory bank is
extremely lightweight, since affine parameters account for less than 2% of a
typical object detector. Furthermore, contrary to previous domain-incremental
learning approaches, we do not require the weather label when testing and
propose to automatically infer the weather condition by a majority voting
linear classifier.