  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.
