What does self-supervised learning utilize in the training process?

Self-supervised learning is a type of machine learning approach that does not rely heavily on labeled datasets. Instead, it makes use of the data itself to generate its own labels. In this context, a portion of the data can serve as a source for creating pseudo-labels, allowing the model to learn patterns and representations without the need for exhaustive manual labeling. This leads to more efficient use of large unlabeled datasets, which are often more accessible than fully labeled ones.

By leveraging a portion of the data for training while allowing the rest to serve as context or input, self-supervised learning models can uncover intricate structures in the data, ultimately leading to enhanced performance in tasks such as classification or feature extraction when applied later with limited labeled data. This characteristic allows self-supervised learning to bridge the gap between unsupervised and supervised learning, maximizing the utility of available data.