Small Sample

Small sample size presents a significant challenge across numerous scientific domains, hindering accurate model training and reliable inference. Current research focuses on developing robust methods to address this limitation, exploring techniques like generative adversarial networks (GANs) for data augmentation, modified Bayesian optimization approaches for improved sample efficiency, and novel algorithms that leverage graph convolutional networks or fully convolutional networks for feature selection and improved model performance in low-data regimes. Overcoming these limitations is crucial for advancing fields ranging from medical image analysis and malware detection to materials science and environmental modeling, where acquiring large datasets is often impractical or impossible.