  Traffic sign recognition is a well-researched problem in computer vision.
However, the state of the art methods works only for frequent sign classes,
which are well represented in training datasets. We consider the task of rare
traffic sign detection and classification. We aim to solve that problem by
using synthetic training data. Such training data is obtained by embedding
synthetic images of signs in the real photos. We propose three methods for
making synthetic signs consistent with a scene in appearance. These methods are
based on modern generative adversarial network (GAN) architectures. Our
proposed methods allow realistic embedding of rare traffic sign classes that
are absent in the training set. We adapt a variational autoencoder for sampling
plausible locations of new traffic signs in images. We demonstrate that using a
mixture of our synthetic data with real data improves the accuracy of both
classifier and detector.
