What is Self-Supervised Learning?
Self-Supervised Learning (SSL) is a learning paradigm in which a machine learning model learns useful features or representations from unlabeled data by generating its own supervisory signals. This is achieved by designing pretext tasks, which are auxiliary tasks that the model solves to learn meaningful representations. The learned representations can then be used for downstream tasks, such as classification or regression, with little or no additional supervision.
Why use Self-Supervised Learning?
Self-Supervised Learning is useful because it allows models to learn from large amounts of unlabeled data, which is often more abundant than labeled data. By learning useful representations from unlabeled data, SSL can improve the performance of downstream tasks and reduce the need for expensive manual labeling.
Examples of Self-Supervised Learning tasks:
- Image inpainting: The model is tasked with predicting the missing pixels in an image when a portion is masked out.
- Image rotation: The model predicts the correct orientation of a rotated image.
- Temporal sequence prediction: The model predicts the next frame in a video or the next word in a sentence.