Unsupervised Pre Training

Unsupervised pre-training refers to the method of pre-training neural networks on unlabelled data through self-supervised auxiliary tasks. Its objective is to leverage a large amount of unlabelled data to learn general feature representations, thereby enhancing the model's generalization ability and performance in downstream tasks. Unsupervised pre-training can effectively alleviate the issue of insufficient labelled data, improving the robustness and adaptability of models, and it has significant application value in fields such as natural language processing and computer vision.