Yes, self-supervised learning can be applied to both supervised and unsupervised tasks. It serves as a bridge between the two paradigms, helping to leverage vast amounts of unlabeled data to improve performance on a range of tasks. In essence, self-supervised learning allows a model to create supervisory signals from the data itself, making it an effective approach in scenarios where labeled data is scarce or expensive to obtain.
For supervised tasks, self-supervised learning can be used to pre-train a model on a large dataset before fine-tuning it on a smaller labeled dataset. For example, in natural language processing, a model can be pre-trained on a large corpus of text using self-supervised techniques like predicting the next word in a sentence. Once the model has learned general language patterns, it can be fine-tuned on a specific task, like sentiment analysis, where labeled data is more limited. This approach can lead to better performance than training from scratch.
On the other hand, self-supervised learning can also be beneficial for unsupervised tasks. It can help in feature extraction, where the model identifies important patterns and representations in the data without any explicit labels. For instance, in image processing, a self-supervised technique might involve training a model to predict the arrangement of image patches. Once this feature extraction is complete, the learned representations can be used for tasks such as clustering images or dimensionality reduction. By enabling models to learn from unlabeled data, self-supervised learning enhances the ability to discover hidden structures in the data, which is particularly valuable in unsupervised tasks.