Can self-supervised learning be applied to both supervised and unsupervised tasks?

Yes, self-supervised learning (SSL) can be applied to both supervised and unsupervised tasks. SSL works by generating labels automatically from the input data, eliminating the need for manual annotation. This approach is flexible enough to support scenarios where labeled data is scarce (common in unsupervised learning) or to improve performance in supervised tasks by pretraining models on unlabeled data. The key idea is that SSL learns useful representations of data by solving “pretext tasks” (e.g., predicting missing parts of an input), which can then be adapted to downstream applications.

In supervised learning, SSL is often used as a pretraining step. For example, in natural language processing (NLP), models like BERT are pretrained using masked language modeling—a self-supervised task where the model predicts missing words in a sentence. Once pretrained, BERT can be fine-tuned on labeled datasets for tasks like sentiment analysis or question answering. Similarly, in computer vision, models like SimCLR use contrastive learning (another SSL method) to pretrain on unlabeled images by comparing augmented versions of the same image. These pretrained models are then fine-tuned for supervised tasks like image classification. SSL here acts as a way to leverage large amounts of unlabeled data to improve generalization in supervised settings.

For unsupervised tasks, SSL directly learns patterns without relying on labels. For instance, autoencoders are trained to reconstruct their input—a self-supervised task—which helps them learn compact representations of data. These representations can then be used for clustering or anomaly detection. Another example is predicting the rotation angle of an image (a pretext task), which forces the model to understand object orientation and structure. The learned features can then be applied to unsupervised tasks like image retrieval or grouping similar images. SSL’s ability to create meaningful representations from raw data makes it a bridge between purely supervised and unsupervised methods, adapting to the needs of the task at hand.