  In the classical supervised learning settings, classifiers are fit with the
assumption of balanced label distributions and produce remarkable results on
the same. In the real world, however, these assumptions often bend and in turn
adversely impact model performance. Identifying bad learners in skewed target
distributions is even more challenging. Thus achieving model robustness under
these "label shift" settings is an important task in autonomous perception. In
this paper, we analyze the impact of label shift on the task of multi-weather
classification for autonomous vehicles. We use this information as a prior to
better assess pedestrian detection in adverse weather. We model the
classification performance as an indicator of robustness under 4 label shift
scenarios and study the behavior of multiple classes of models. We propose
t-RAIN a similarity mapping technique for synthetic data augmentation using
large scale generative models and evaluate the performance on DAWN dataset.
This mapping boosts model test accuracy by 2.1, 4.4, 1.9, 2.7 % in no-shift,
fog, snow, dust shifts respectively. We present state-of-the-art pedestrian
detection results on real and synthetic weather domains with best performing
82.69 AP (snow) and 62.31 AP (fog) respectively.
