Binary Gaussian Copula Synthesis for Early Dialysis Prediction in CKD Patients
The author proposes the Binary Gaussian Copula Synthesis (BGCS) as a novel data augmentation method tailored for binary medical datasets to enhance early dialysis prediction in CKD patients.
Adversarial Automatic Mixup: Enhancing Image Classification with Adversarial Training
AdAutomixup proposes an adversarial automatic mixup augmentation approach to generate challenging samples for robust image classification. By optimizing the classifier and mixup sample generator adversarially, it aims to improve generalization performance.
Phased Data Augmentation for Training a Likelihood-Based Generative Model with Limited Data
Introducing "phased data augmentation" as a novel technique to enhance training of likelihood-based generative models with limited data.
DreamDA: Generative Data Augmentation with Diffusion Models
Proposing DreamDA for diverse and high-quality data synthesis using diffusion models.
DreamDA: Generative Data Augmentation with Diffusion Models
Proposing DreamDA framework for diverse image synthesis using diffusion models.
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