  This work investigates the unexplored usability of self-supervised
representation learning in the direction of functional knowledge transfer. In
this work, functional knowledge transfer is achieved by joint optimization of
self-supervised learning pseudo task and supervised learning task, improving
supervised learning task performance. Recent progress in self-supervised
learning uses a large volume of data, which becomes a constraint for its
applications on small-scale datasets. This work shares a simple yet effective
joint training framework that reinforces human-supervised task learning by
learning self-supervised representations just-in-time and vice versa.
Experiments on three public datasets from different visual domains, Intel
Image, CIFAR, and APTOS, reveal a consistent track of performance improvements
on classification tasks during joint optimization. Qualitative analysis also
supports the robustness of learnt representations. Source code and trained
models are available on GitHub.
