  Self-Supervised Learning (SSL) has emerged as the solution of choice to learn
transferable representations from unlabeled data. However, SSL requires to
build samples that are known to be semantically akin, i.e. positive views.
Requiring such knowledge is the main limitation of SSL and is often tackled by
ad-hoc strategies e.g. applying known data-augmentations to the same input. In
this work, we formalize and generalize this principle through Positive Active
Learning (PAL) where an oracle queries semantic relationships between samples.
PAL achieves three main objectives. First, it unveils a theoretically grounded
learning framework beyond SSL, based on similarity graphs, that can be extended
to tackle supervised and semi-supervised learning depending on the employed
oracle. Second, it provides a consistent algorithm to embed a priori knowledge,
e.g. some observed labels, into any SSL losses without any change in the
training pipeline. Third, it provides a proper active learning framework
yielding low-cost solutions to annotate datasets, arguably bringing the gap
between theory and practice of active learning that is based on
simple-to-answer-by-non-experts queries of semantic relationships between
inputs.
