  It is known that, without awareness of the process, our brain appears to
focus on the general shape of objects rather than superficial statistics of
context. On the other hand, learning autonomously allows discovering invariant
regularities which help generalization. In this work, we propose a learning
framework to improve the shape bias property of self-supervised methods. Our
method learns semantic and shape biased representations by integrating domain
diversification and jigsaw puzzles. The first module enables the model to
create a dynamic environment across arbitrary domains and provides a domain
exploration vs. exploitation trade-off, while the second module allows the
model to explore this environment autonomously. This universal framework does
not require prior knowledge of the domain of interest. Extensive experiments
are conducted on several domain generalization datasets, namely, PACS,
Office-Home, VLCS, and Digits. We show that our framework outperforms
state-of-the-art domain generalization methods by a large margin.
