  This paper investigates methods for improving generative data augmentation
for deep learning. 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. This is
because the synthetic samples do not perfectly represent class categories in
real data and uniform sampling does not necessarily provide useful samples for
tasks. In this paper, we present a novel strategy for generative data
augmentation called meta generative regularization (MGR). To avoid the
degradation of generative data augmentation, MGR utilizes synthetic samples in
the regularization term for feature extractors instead of in the loss function,
e.g., cross-entropy. These synthetic samples are dynamically determined to
minimize the validation losses through meta-learning. We observed that MGR can
avoid the performance degradation of na\"ive generative data augmentation and
boost the baselines. Experiments on six datasets showed that MGR is effective
particularly when datasets are smaller and stably outperforms baselines.
