With development of deep learning, researchers have developed generative
models in generating realistic images. One of such generative models, a
PixelCNNs model with Vector Quantized Variational AutoEncoder 2 (VQ-VAE-2), can
generate more various images than other models. However, a PixelCNNs model with
VQ-VAE-2, I call it PC-VQ2, requires sufficiently much training data like other
deep learning models. Its practical applications are often limited in domains
where collecting sufficient data is not difficult. To solve the problem,
researchers have recently proposed more data-efficient methods for training
generative models with limited unlabeled data from scratch. However, no such
methods in PC-VQ2s have been researched. This study provides the first step in
this direction, considering generation of images using PC-VQ2s and limited
unlabeled data. In this study, I propose a training strategy for training a
PC-VQ2 with limited data from scratch, phased data augmentation. In the
strategy, ranges of parameters of data augmentation is narrowed in phases
through learning. Quantitative evaluation shows that the phased data
augmentation enables the model with limited data to generate images competitive
with the one with sufficient data in diversity and outperforming it in
fidelity. The evaluation suggests that the proposed method should be useful for
training a PC-VQ2 with limited data efficiently to generate various and natural
images.

通过阶段性数据增强的方式，提出了一种对于有限数据进行有效训练 PixelCNNs 模型的方法，并在数量和质量上优于传统的大数据训练模型。