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.

翻译：随着深度学习的发展，研究者已开发出能生成逼真图像的生成模型。其中，结合向量量化变分自编码器2（VQ-VAE-2）的PixelCNNs模型相比其他模型能生成更多样化的图像。然而，我将这种VQ-VAE-2驱动的PixelCNNs模型简称为PC-VQ2，它与其他深度学习模型一样需要充足的训练数据。在实际应用中，当数据收集不存在困难的领域，其应用常受此限制。为解决该问题，研究者近期提出了更多数据高效的方法，用于从零开始利用有限的无标注数据训练生成模型。但在PC-VQ2中尚未有此类方法的研究。本研究迈出了该方向的第一步，探索利用PC-VQ2和有限的无标注数据生成图像。本文提出一种从零开始用有限数据训练PC-VQ2的训练策略——阶段式数据增强。该策略通过阶段性学习逐步缩窄数据增强的参数范围。定量评估表明，阶段式数据增强能使有限数据训练的模型在图像多样性方面达到与充足数据训练模型相当的水平，且保真度更优。评估结果显示，该方法能有效利用有限数据高效训练PC-VQ2，生成多样且自然的图像。