  Self-supervised learning (SlfSL), aiming at learning feature representations
through ingeniously designed pretext tasks without human annotation, has
achieved compelling progress in the past few years. Very recently, SlfSL has
also been identified as a promising solution for semi-supervised learning
(SemSL) since it offers a new paradigm to utilize unlabeled data. This work
further explores this direction by proposing to couple SlfSL with SemSL. Our
insight is that the prediction target in SemSL can be modeled as the latent
factor in the predictor for the SlfSL target. Marginalizing over the latent
factor naturally derives a new formulation which marries the prediction targets
of these two learning processes. By implementing this idea through a
simple-but-effective SlfSL approach -- rotation angle prediction, we create a
new SemSL approach called Conditional Rotation Angle Estimation (CRAE).
Specifically, CRAE is featured by adopting a module which predicts the image
rotation angle conditioned on the candidate image class. Through experimental
evaluation, we show that CRAE achieves superior performance over the other
existing ways of combining SlfSL and SemSL. To further boost CRAE, we propose
two extensions to strengthen the coupling between SemSL target and SlfSL target
in basic CRAE. We show that this leads to an improved CRAE method which can
achieve the state-of-the-art SemSL performance.
