Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

Medical researchers at Mass General Brigham say the self-supervised foundational model can identify inherent features from ...

Abstract: In medical image analysis, obtaining extensive well-annotated data is expensive and laborious in that it needs expert knowledge of clinicians. Semi-supervised learning (SSL) is an effective ...

In the training phase, both supervised and self-supervised learning are used to create a classification task and a generation task, respectively. Then, the classifier fθ and generator fg are used to ...

Abstract: We introduce LCMatch, a novel semi-supervised scene classification framework designed to enhance the performance of remote sensing image classification. Our method improves upon the existing ...