  Contrastive Learning (CL) has been successfully applied to classification and
other downstream tasks related to concrete concepts, such as objects contained
in the ImageNet dataset. No attempts seem to have been made so far in applying
this promising scheme to more abstract entities. A prominent example of these
could be the concept of (discrete) Quantity. CL can be frequently interpreted
as a self-supervised scheme guided by some profound and ubiquitous conservation
principle (e.g. conservation of identity in object classification tasks). In
this introductory work we apply a suitable conservation principle to the
semi-abstract concept of natural numbers by which discrete quantities can be
estimated or predicted. We experimentally show, by means of a toy problem, that
contrastive learning can be trained to count at a glance with high accuracy
both at human as well as at super-human ranges.. We compare this with the
results of a trained-to-count at a glance supervised learning (SL) neural
network scheme of similar architecture. We show that both schemes exhibit
similar good performance on baseline experiments, where the distributions of
the training and testing stages are equal. Importantly, we demonstrate that in
some generalization scenarios, where training and testing distributions differ,
CL boasts more robust and much better error performance.
