Generative models excel in creating realistic images, yet their dependency on extensive datasets for training presents significant challenges, especially in domains where data collection is costly or challenging. Current data-efficient methods largely focus on GAN architectures, leaving a gap in training other types of generative models. Our study introduces "phased data augmentation" as a novel technique that addresses this gap by optimizing training in limited data scenarios without altering the inherent data distribution. By limiting the augmentation intensity throughout the learning phases, our method enhances the model's ability to learn from limited data, thus maintaining fidelity. Applied to a model integrating PixelCNNs with VQ-VAE-2, our approach demonstrates superior performance in both quantitative and qualitative evaluations across diverse datasets. This represents an important step forward in the efficient training of likelihood-based models, extending the usefulness of data augmentation techniques beyond just GANs.

翻译：生成模型在创建逼真图像方面表现出色，但它们对大规模数据集的依赖带来了显著挑战，尤其是在数据收集成本高昂或困难的领域。当前的数据高效方法主要集中在GAN架构上，导致其他类型生成模型的训练存在空白。本研究提出“阶段式数据增强”作为一种新技术，通过在不改变固有数据分布的前提下优化有限数据场景下的训练，从而填补了这一空白。通过在学习阶段中限制增强强度，我们的方法提升了模型从有限数据中学习的能力，从而保持了保真度。将该方法应用于整合PixelCNN与VQ-VAE-2的模型后，我们在多个数据集上的定量和定性评估中均展示了卓越性能。这代表了在高效训练基于似然模型方面迈出的重要一步，将数据增强技术的应用范围扩展到了GAN之外。