Yes, self-supervised learning is applicable to various types of data, including images, text, and audio. This technique allows models to learn representations from the data itself without the need for extensive labeled datasets. By creating tasks where the model predicts a part of the data based on other parts, it can effectively learn meaningful features across different domains.
For images, self-supervised learning might involve training a model to fill in missing parts of an image or to predict the rotation angle of a rotated image. These tasks help the model understand visual concepts and relationships without the necessity of labeled images. Some popular methods in this area include contrastive learning and predictive coding, which have shown promise in enhancing the accuracy of image recognition systems.
In the realm of text data, models often use techniques like masked language modeling. For example, models like BERT are trained to predict masked words in a sentence. This task encourages the model to grasp context, grammar, and semantics, contributing to improved performance in various natural language processing tasks. Similarly, audio data can also benefit from self-supervised learning, where models learn to predict future sound segments or fill in missing parts of audio clips. Overall, self-supervised learning is versatile and continues to be a valuable approach across different types of data.