  We explore the zero-shot setting for day-night domain adaptation. The
traditional domain adaptation setting is to train on one domain and adapt to
the target domain by exploiting unlabeled data samples from the test set. As
gathering relevant test data is expensive and sometimes even impossible, we
remove any reliance on test data imagery and instead exploit a visual inductive
prior derived from physics-based reflection models for domain adaptation. We
cast a number of color invariant edge detectors as trainable layers in a
convolutional neural network and evaluate their robustness to illumination
changes. We show that the color invariant layer reduces the day-night
distribution shift in feature map activations throughout the network. We
demonstrate improved performance for zero-shot day to night domain adaptation
on both synthetic as well as natural datasets in various tasks, including
classification, segmentation and place recognition.
