Investigating Semantic Augmentation in Virtual Environments for Image Segmentation Using Convolutional Neural Networks.

ABSTRACT: Collecting real-world data for the training of neural networks is enormously time-consuming and expensive. As such, the concept of virtualizing the domain and creating synthetic data has been analyzed in many instances. This virtualization offers many possibilities of changing the domain, and with that, enabling the relatively fast creation of data. It also offers the chance to enhance necessary augmentations with additional semantic information when compared with conventional augmentation methods. This raises the question of whether such semantic changes, which can be seen as augmentations of the virtual domain, contribute to better results for neural networks, when trained with data augmented this way. In this paper, a virtual dataset is presented, including semantic augmentations and au

SUBMITTER: Ganter J 

PROVIDER: S-EPMC8404924 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature