Self Supervised Pretext Task

Self-supervised pretext tasks leverage unlabeled data to pre-train models by solving auxiliary tasks, improving performance on downstream tasks with limited labeled data. Current research focuses on designing effective pretext tasks for various modalities (images, videos, graphs), often employing convolutional neural networks, vision transformers, and graph neural networks, and exploring strategies like curriculum learning and multi-teacher knowledge distillation to optimize the pre-training process. This approach is particularly valuable in domains with scarce labeled data, such as medical imaging and few-shot learning, enabling the development of more robust and efficient models for diverse applications.