1. Illium, S., Griffin, G., Kölle, M., Zorn, M., Nüßlein, J., and Linnhoff-Popien, C. 2022. VoronoiPatches: Evaluating A New Data Augmentation Method. arXiv preprint arXiv:2212.10054.

Organism Network Architecture

This study introduces VoronoiPatches (VP), a novel data augmentation algorithm that enhances Convolutional Neural Networks’ performance by using non-linear recombination of image information. VP distinguishes itself by utilizing small, convex polygon-shaped patches in random layouts to redistribute information within an image, potentially smoothing transitions between patches and the original image. This method has shown to outperform existing data augmentation techniques in reducing model variance and overfitting, thus improving the robustness of CNN models on unseen data. [Illium et al. 2022]

:trophy: Our work was awarded the Best Poster Award at ICAART 2023 :trophy: