A number of different architectures and loss functions have been applied to the problem of self-supervised learning (SSL), with the goal of developing embeddings that provide the best possible pre-training for as-yet-unknown, lightly supervised downstream tasks. One of these SSL criteria is to maximize the entropy of a set of embeddings in some compact space. But the goal of maximizing the embedding entropy often depends—whether explicitly or implicitly—upon high dimensional entropy estimates, which typically perform poorly in more than a few dimensions. In this paper, we motivate a simple maximum-entropy criterion, defined in terms of easy-to-estimate, low-dimensional constraints, and demonstrate that using it to continue training an already-trained SSL model for only a handful of epochs leads to a consistent and, in some cases, significant improvement in performance.