Good representations are expressive and make efficient use of the given dimensionality.

We want the representations to be variant to contextual changes that are essential to a task and invariant to changes related to factors that we cannot control nor care about. 

While these invariances can largely be enforced through task-specific data augmentation, the efficient use of the dimensionality of the representations has to be achieved through clever algorithms.

One way to look at this quality is through the variance of the representations, which shall not collapse for a given class of inputs. 

While many recently proposed self-supervised learning algorithms prevent a collapse of the embedding-space implicitly through various methods like contrasting samples in a batch [SimCLR] or clustering [