Self Supervised Model

Self-supervised learning (SSL) models aim to learn useful data representations without relying on labeled data, leveraging vast amounts of unlabeled information for pre-training. Current research focuses on improving the efficiency and generalizability of these models across diverse domains, including speech recognition, image analysis (e.g., medical imaging, satellite imagery), and video processing, often employing architectures like Vision Transformers and convolutional neural networks. This approach is significant because it addresses the limitations of supervised learning in data-scarce scenarios and offers potential for improved performance and robustness in various applications, while also raising important considerations regarding bias and privacy.