Self Supervised Method

Self-supervised learning (SSL) aims to train models on unlabeled data by formulating pretext tasks that implicitly capture underlying data structure. Current research focuses on improving the efficiency and robustness of SSL across diverse modalities (image, audio, video, medical imaging), exploring architectures like transformers and autoencoders, and employing techniques such as contrastive learning, masked image modeling, and clustering. These advancements are significant because they reduce reliance on expensive labeled datasets, enabling the development of powerful models for various applications, including speech recognition, image reconstruction, and medical image analysis.