Self Supervised Learning

Self-supervised learning (SSL) aims to utilize large amounts of unlabeled data by generating labels from the structure or features inherent in the data itself, thereby training models in a supervised manner. This approach effectively reduces the cost of annotation while enhancing the model's ability to learn potential features from the data, and it is widely applied in representation learning, especially in computer vision tasks such as point cloud registration.