  The typical contrastive self-supervised algorithm uses a similarity measure
in latent space as the supervision signal by contrasting positive and negative
images directly or indirectly. Although the utility of self-supervised
algorithms has improved recently, there are still bottlenecks hindering their
widespread use, such as the compute needed. In this paper, we propose a module
that serves as an additional objective in the self-supervised contrastive
learning paradigm. We show how the inclusion of this module to regress the
parameters of an affine transformation or homography, in addition to the
original contrastive objective, improves both performance and learning speed.
Importantly, we ensure that this module does not enforce invariance to the
various components of the affine transform, as this is not always ideal. We
demonstrate the effectiveness of the additional objective on two recent,
popular self-supervised algorithms. We perform an extensive experimental
analysis of the proposed method and show an improvement in performance for all
considered datasets. Further, we find that although both the general homography
and affine transformation are sufficient to improve performance and
convergence, the affine transformation performs better in all cases.
