What is an unsupervised pretext task in self-supervised learning?

An unsupervised pretext task in self-supervised learning is a method where a model learns useful data representations by solving an artificial task created from unlabeled data. Unlike supervised learning, which relies on human-annotated labels, self-supervised learning generates its own “labels” by exploiting the inherent structure of the data. Pretext tasks are designed to force the model to capture patterns or relationships in the data, which can later be transferred to downstream tasks like classification or object detection. The goal is to pretrain a model on these tasks so it learns general features without requiring manual labeling.

A common example of a pretext task is predicting the rotation angle of an image. Suppose you take an unlabeled image dataset and rotate each image by a random angle (e.g., 0°, 90°, 180°, 270°). The model is then trained to predict the rotation angle applied to each image. To solve this, the model must learn features like object orientation, edges, and spatial relationships, which are valuable for tasks like image recognition. Another example is masked language modeling, used in models like BERT, where parts of a text sequence are hidden, and the model predicts the missing words based on context. These tasks are unsupervised because the “labels” (rotation angles or missing words) are derived directly from the data itself.

Pretext tasks are effective because they encourage the model to learn transferable representations. For instance, in computer vision, training a model to reconstruct missing parts of an image (inpainting) teaches it to understand textures and object shapes. In audio processing, predicting whether two audio clips are temporally adjacent helps the model learn temporal dependencies. Developers can design pretext tasks tailored to their data type and domain—for example, predicting future frames in video data or identifying whether two image patches belong to the same object. While the pretext task itself may not solve a real-world problem, the learned features can be fine-tuned with minimal labeled data for specific applications, reducing reliance on large labeled datasets.