Overfitting In Contrastive Learning?

TL;DR

This paper investigates the occurrence and mechanisms of overfitting in unsupervised contrastive learning, an area less explored compared to supervised learning, revealing that overfitting can indeed happen in this context.

Contribution

It provides the first detailed analysis of overfitting phenomena and mechanisms specifically in unsupervised contrastive learning.

Findings

Overfitting can occur in contrastive learning.

The mechanisms behind overfitting in contrastive learning are identified.

Insights into how overfitting impacts unsupervised contrastive models.

Abstract

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 unsupervised 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.