Self Supervised Task

Self-supervised learning aims to train models on unlabeled data by creating pretext tasks that implicitly capture underlying data structure, improving downstream performance on labeled tasks. Current research focuses on developing effective pretext tasks for various data modalities (images, text, graphs, time series) and integrating self-supervision with existing architectures like transformers, autoencoders, and GANs, often within multi-task or meta-learning frameworks. This approach is significant because it reduces reliance on expensive labeled data, leading to more efficient and robust models across diverse applications, including image classification, natural language processing, and anomaly detection.