Regularizing Neural Networks with Meta-Learning Generative Models Shin'ya Yamaguchi

Generative data augmentation leverages the synthetic samples produced by generative models as an additional dataset for classification with small dataset settings. A key challenge of generative data augmentation is that the synthetic data contain uninformative samples that degrade accuracy.