Self Supervised Technique

Self-supervised learning techniques aim to train machine learning models on unlabeled data by creating pretext tasks that implicitly capture underlying data structure. Current research focuses on developing efficient algorithms, such as contrastive learning and Siamese networks, often incorporating multiple architectures (e.g., CNNs and Transformers) for improved performance and robustness across diverse data modalities (images, audio, text, time series). These methods are proving valuable in various applications, including healthcare, anomaly detection, and natural language processing, particularly where labeled data is scarce or expensive to obtain, thereby advancing the field of representation learning and enabling more efficient model training.

Papers