  In self-supervised learning, a system is tasked with achieving a surrogate
objective by defining alternative targets on a set of unlabeled data. The aim
is to build useful representations that can be used in downstream tasks,
without costly manual annotation. In this work, we propose a novel
self-supervised formulation of relational reasoning that allows a learner to
bootstrap a signal from information implicit in unlabeled data. Training a
relation head to discriminate how entities relate to themselves
(intra-reasoning) and other entities (inter-reasoning), results in rich and
descriptive representations in the underlying neural network backbone, which
can be used in downstream tasks such as classification and image retrieval. We
evaluate the proposed method following a rigorous experimental procedure, using
standard datasets, protocols, and backbones. Self-supervised relational
reasoning outperforms the best competitor in all conditions by an average 14%
in accuracy, and the most recent state-of-the-art model by 3%. We link the
effectiveness of the method to the maximization of a Bernoulli log-likelihood,
which can be considered as a proxy for maximizing the mutual information,
resulting in a more efficient objective with respect to the commonly used
contrastive losses.
