Overfitting In Contrastive Learning?
This addresses a gap in understanding overfitting for unsupervised learning, which is incremental as it extends known supervised concepts to a new context.
The paper investigates overfitting in unsupervised contrastive learning, demonstrating that it can occur and explaining the underlying mechanism.
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.