There are numerous ways to blend self-supervised learning and unsupervised learning techniques. A few popular techniques in semi-supervised learning are:
Self-training - The model first gets trained using labeled data, and then it makes predictions on the unlabeled data. The most confident predictions get added to the training set, and the process continues.
Co-training - Two separate models get trained using different views of the labeled data. These models predict labels on unlabelled data, and the most confident predictions possibly improved through self-supervised learning, get added to the training set.
Graph-based methods - Unlabeled data set gets connected to labeled data through graph structures, and these connections, potentially enhanced by self-supervised learning, help algorithms propagate labels to better model the dataset.