pretexttask
A pretext task, also known as a pretraining task, is a method used in machine learning, particularly in the context of unsupervised or self-supervised learning, to facilitate the development of useful representations without requiring labeled data. In this approach, a neural network learns to solve a task that is designed to be related to the main task but does not directly involve the ultimate goal. The idea is that by mastering the pretext task, the model acquires features and knowledge that can be transferred to perform better on the downstream task.
Pretext tasks are widely used in natural language processing and computer vision. In NLP, common examples include
The main advantage of using pretext tasks is that they enable models to leverage large amounts of
Despite their benefits, the effectiveness of a pretext task depends on how well it aligns with the