Abstract

Shape bias plays an important role in self-supervised learning paradigm. The ultimate goal in self-supervised learning is to capture a representation that is based as much as possible on the semantic of objects (i.e. shape bias) and not on individual objects’ peripheral features. This is inline with how human learns in general; our brain unconsciously focuses on the general shape of objects rather than superficial statistics of context. On the other hand, unsupervised representation learning allows discovering label-invariant features which helps generalization of the model. Inspired by these observations, we propose a learning framework to improve the learning performance of self-supervised methods by further hitching their learning process to shape bias. Using distinct modules, 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 it to explore this environment autonomously. The proposed framework is universally adaptable since it does