  We describe an iterative active-learning algorithm to recognise rare traffic
signs. A standard ResNet is trained on a training set containing only a single
sample of the rare class. We demonstrate that by sorting the samples of a
large, unlabeled set by the estimated probability of belonging to the rare
class, we can efficiently identify samples from the rare class. This works
despite the fact that this estimated probability is usually quite low. A
reliable active-learning loop is obtained by labeling these candidate samples,
including them in the training set, and iterating the procedure. Further, we
show that we get similar results starting from a single synthetic sample. Our
results are important as they indicate a straightforward way of improving
traffic-sign recognition for automated driving systems. In addition, they show
that we can make use of the information hidden in low confidence outputs, which
is usually ignored.
