  Recent work on few-shot learning \cite{tian2020rethinking} showed that
quality of learned representations plays an important role in few-shot
classification performance. On the other hand, the goal of self-supervised
learning is to recover useful semantic information of the data without the use
of class labels. In this work, we exploit the complementarity of both paradigms
via a multi-task framework where we leverage recent self-supervised methods as
auxiliary tasks. We found that combining multiple tasks is often beneficial,
and that solving them simultaneously can be done efficiently. Our results
suggest that self-supervised auxiliary tasks are effective data-dependent
regularizers for representation learning. Our code is available at:
\url{https://github.com/nathanielsimard/improving-fs-ssl}.
