  We propose the first qualitative hypothesis characterizing the behavior of
visual transformation based self-supervision, called the VTSS hypothesis. Given
a dataset upon which a self-supervised task is performed while predicting
instantiations of a transformation, the hypothesis states that if the predicted
instantiations of the transformations are already present in the dataset, then
the representation learned will be less useful. The hypothesis was derived by
observing a key constraint in the application of self-supervision using a
particular transformation. This constraint, which we term the transformation
conflict for this paper, forces a network learn degenerative features thereby
reducing the usefulness of the representation. The VTSS hypothesis helps us
identify transformations that have the potential to be effective as a
self-supervision task. Further, it helps to generally predict whether a
particular transformation based self-supervision technique would be effective
or not for a particular dataset. We provide extensive evaluations on CIFAR 10,
CIFAR 100, SVHN and FMNIST confirming the hypothesis and the trends it
predicts. We also propose novel cost-effective self-supervision techniques
based on translation and scale, which when combined with rotation outperforms
all transformations applied individually. Overall, this paper aims to shed
light on the phenomenon of visual transformation based self-supervision.
