  Sketch-based understanding is a critical component of human cognitive
learning and is a primitive communication means between humans. This topic has
recently attracted the interest of the computer vision community as sketching
represents a powerful tool to express static objects and dynamic scenes.
Unfortunately, despite its broad application domains, the current sketch-based
models strongly rely on labels for supervised training, ignoring knowledge from
unlabeled data, thus limiting the underlying generalization and the
applicability. Therefore, we present a study about the use of unlabeled data to
improve a sketch-based model. To this end, we evaluate variations of VAE and
semi-supervised VAE, and present an extension of BYOL to deal with sketches.
Our results show the superiority of sketch-BYOL, which outperforms other
self-supervised approaches increasing the retrieval performance for known and
unknown categories. Furthermore, we show how other tasks can benefit from our
proposal.
