MoireDB: Formula-generated Interference-fringe Image Dataset
Journal:
arXiv
Published Date:
Feb 3, 2025
Abstract
Image recognition models have struggled to treat recognition robustness to
real-world degradations. In this context, data augmentation methods like PixMix
improve robustness but rely on generative arts and feature visualizations
(FVis), which have copyright, drawing cost, and scalability issues. We propose
MoireDB, a formula-generated interference-fringe image dataset for image
augmentation enhancing robustness. MoireDB eliminates copyright concerns,
reduces dataset assembly costs, and enhances robustness by leveraging illusory
patterns. Experiments show that MoireDB augmented images outperforms
traditional Fractal arts and FVis-based augmentations, making it a scalable and
effective solution for improving model robustness against real-world
degradations.
