Self Supervised Pre Training

Self-supervised pre-training (SSP) aims to learn robust feature representations from unlabeled data by training models on pretext tasks, improving their performance on downstream tasks with limited labeled data. Current research focuses on optimizing the alignment between pre-training and fine-tuning stages, developing efficient methods like dataset distillation and exploring various architectures including Vision Transformers, masked autoencoders, and diffusion models, often within contrastive or self-distillation frameworks. SSP's significance lies in its ability to leverage vast amounts of unlabeled data, enhancing model performance across diverse domains like computer vision, natural language processing, and medical imaging, particularly when labeled data is scarce or expensive to obtain.