Self-supervised learning can utilize various types of data, primarily categorized into images, text, audio, and video. Each of these data types offers unique challenges and opportunities for learning without requiring labeled data. This approach enables models to learn useful representations directly from the raw data itself by creating auxiliary tasks that help discover structure and patterns.
For instance, in the context of images, self-supervised learning can involve tasks such as predicting missing parts of an image or identifying the rotation angle of a rotated image. These tasks help models learn features that are relevant for various downstream applications, such as image classification or object detection. Similarly, for text data, a common approach is to predict the next word in a sentence, where models learn from the context provided by surrounding words, allowing them to capture semantic meanings and relationships without needing annotated datasets.
In addition to images and text, audio and video data can also be effectively utilized for self-supervised learning. For audio, tasks can include predicting future audio frames or identifying segments within audio clips. These tasks help in capturing the temporal dynamics of sound. For video, models might learn by predicting the next frame in a sequence or identifying actions within a video clip, which aids in understanding motion and context. Overall, the adaptability of self-supervised learning techniques across various data types makes it a powerful method for training models in situations where labeled data is limited or scarce.