Background: The performance of a classification algorithm eventually reaches a point of diminishing returns, where the additional sample added does not improve the results. Thus, there is a need to ...

Abstract: Collecting a substantial number of labeled samples is infeasible in many real-world scenarios, thereby bringing out challenges for supervised classification. The research on Few-Shot ...

Abstract: This paper defines a positive and unlabeled classification problem for standard GANs, which then leads to a novel technique to stabilize the training of the discriminator in GANs and deal ...