Pretext Task

Pretext tasks are auxiliary learning objectives used in self-supervised learning to train models on unlabeled data before applying them to a target task. Current research focuses on developing effective pretext tasks for various data modalities (images, videos, tabular data, graphs) and exploring their impact on downstream performance across diverse applications, including robot localization, medical image analysis, and time series classification. These techniques are significant because they address the limitations of supervised learning by leveraging vast amounts of unlabeled data, leading to improved model performance, especially in data-scarce scenarios. The resulting pre-trained models often exhibit enhanced generalization and robustness compared to models trained solely on labeled data.