Self Supervised Framework

Self-supervised learning frameworks aim to train deep learning models using unlabeled data, overcoming limitations of data scarcity and annotation costs prevalent in many fields. Current research focuses on developing novel self-supervised objectives and architectures, including contrastive learning, masked autoencoders, and diffusion models, often applied within transformer-based models. These frameworks are proving highly effective across diverse applications, from medical image analysis and video summarization to improving the robustness and generalization of models in various domains, ultimately advancing the capabilities of AI systems. The resulting pre-trained models often significantly outperform those trained with limited supervised data, particularly in low-data regimes.