Data Augmentation (DA) is a technique to increase the quantity and diversity of the training data, and by that alleviate overfitting and improve generalisation. However, standard DA produces synthetic data for augmentation with limited diversity. Generative Adversarial Networks (GANs) may unlock additional information in a dataset by generating synthetic samples having the appearance of real images. However, these models struggle to simultaneously address three key requirements: fidelity and high-quality samples; diversity and mode coverage; and fast sampling. Indeed, GANs generate high-quality samples rapidly, but have poor mode coverage, limiting their adoption in DA applications. We propose LatentAugment, a DA strategy that overcomes the low diversity of GANs, opening up for use in DA applications. Without external supervision, LatentAugment modifies latent vectors and moves them into latent space regions to maximise the synthetic images' diversity and fidelity. It is also agnostic to the dataset and the downstream task. A wide set of experiments shows that LatentAugment improves the generalisation of a deep model translating from MRI-to-CT beating both standard DA as well GAN-based sampling. Moreover, still in comparison with GAN-based sampling, LatentAugment synthetic samples show superior mode coverage and diversity. Code is available at: https://github.com/ltronchin/LatentAugment.

翻译：数据增强（DA）是一种通过增加训练数据的数量和多样性来缓解过拟合并提升泛化能力的技术。然而，标准DA生成的合成数据多样性有限。生成对抗网络（GAN）能够生成具备真实图像外观的合成样本，从而可能解锁数据集中的额外信息。但这类模型难以同时满足三个关键要求：保真度与高质量样本、多样性及模式覆盖、快速采样。尽管GAN能快速生成高质量样本，但其模式覆盖能力较差，限制了其在DA应用中的采用。我们提出LatentAugment——一种克服GAN低多样性缺陷的DA策略，从而开拓其在DA场景中的应用。无需外部监督，LatentAugment通过修改潜在向量并将其移动至潜在空间特定区域，以最大化合成图像的多样性与保真度。该方法对数据集和下流任务均具有无关性。大量实验表明，LatentAugment能提升从MRI到CT图像翻译的深度模型泛化能力，其性能优于标准DA和基于GAN的采样方法。此外，与基于GAN的采样相比，LatentAugment生成的合成样本在模式覆盖和多样性方面表现更优。代码开源地址：https://github.com/ltronchin/LatentAugment。