  The standard approach to modern self-supervised learning is to generate
random views through data augmentations and minimise a loss computed from the
representations of these views. This inherently encourages invariance to the
transformations that comprise the data augmentation function. In this work, we
show that adding a module to constrain the representations to be predictive of
an affine transformation improves the performance and efficiency of the
learning process. The module is agnostic to the base self-supervised model and
manifests in the form of an additional loss term that encourages an aggregation
of the encoder representations to be predictive of an affine transformation
applied to the input images. We perform experiments in various modern
self-supervised models and see a performance improvement in all cases. Further,
we perform an ablation study on the components of the affine transformation to
understand which of them is affecting performance the most, as well as on key
architectural design decisions.
