MoireDB: Formula-generated Interference-fringe Image Dataset
Yuto Matsuo, Ryo Hayamizu, Hirokatsu Kataoka, Akio Nakamura

TL;DR
MoireDB is a novel, formula-generated interference-fringe image dataset designed to improve image recognition robustness, addressing issues of copyright, cost, and scalability inherent in previous augmentation methods.
Contribution
It introduces a scalable, copyright-free dataset for image augmentation that enhances model robustness against real-world degradations.
Findings
MoireDB-augmented images outperform fractal arts and FVis-based methods.
The dataset reduces costs and copyright concerns.
Enhanced robustness demonstrated in experiments.
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDigital Imaging for Blood Diseases · Image Retrieval and Classification Techniques
