  Overfitting describes a machine learning phenomenon where the model fits too
closely to the training data, resulting in poor generalization. While this
occurrence is thoroughly documented for many forms of supervised learning, it
is not well examined in the context of \underline{un}supervised learning. In
this work we examine the nature of overfitting in unsupervised contrastive
learning. We show that overfitting can indeed occur and the mechanism behind
overfitting.
