Whilst computer vision models built using self-supervised approaches are now commonplace, some
important questions remain. Do self-supervised models learn highly redundant channel features? What
if a self-supervised network could dynamically select the important channels and get rid of the unnecessary ones? Currently, convnets pre-trained with self-supervision have obtained comparable performance
on downstream tasks in comparison to their supervised counterparts in computer vision. However, there
are drawbacks to self-supervised models including their large numbers of parameters, computationally expensive training strategies and a clear need for faster inference on downstream tasks. In this work, our
goal is to address the latter by studying how a standard channel selection method developed for supervised
learning can be applied to networks trained with self-supervision. We validate our findings on a range of
target budgets td for channel computation on image classification task across different datasets, specifically
CIFAR-10, CIFAR-100, and ImageNet-100, obtaining comparable performance to that of the original network when selecting all channels but at a significant reduction in computation reported in terms of FLOPs.