MoireMix: A Formula-Based Data Augmentation for Improving Image Classification Robustness

• URL: http://arxiv.org/abs/2603.25109v1
• Date: Thu, 26 Mar 2026 07:29:44 GMT
• Title: MoireMix: A Formula-Based Data Augmentation for Improving Image Classification Robustness
• Abstract summary: We propose a lightweight augmentation method that procedurally generates Moire textures on-the-fly using a closed-form mathematical formulation.<n>The proposed method consistently improves robustness across multiple benchmarks, including ImageNet-C, ImageNet-R, and adversarial benchmarks.<n>These results suggest that analytic interference patterns provide a practical and efficient alternative to data-driven generative methods.
• Score: 37.507208770129296
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Data augmentation is a key technique for improving the robustness of image classification models. However, many recent approaches rely on diffusion-based synthesis or complex feature mixing strategies, which introduce substantial computational overhead or require external datasets. In this work, we explore a different direction: procedural augmentation based on analytic interference patterns. Unlike conventional augmentation methods that rely on stochastic noise, feature mixing, or generative models, our approach exploits Moire interference to generate structured perturbations spanning a wide range of spatial frequencies. We propose a lightweight augmentation method that procedurally generates Moire textures on-the-fly using a closed-form mathematical formulation. The patterns are synthesized directly in memory with negligible computational cost (0.0026 seconds per image), mixed with training images during training, and immediately discarded, enabling a storage-free augmentation pipeline without external data. Extensive experiments with Vision Transformers demonstrate that the proposed method consistently improves robustness across multiple benchmarks, including ImageNet-C, ImageNet-R, and adversarial benchmarks, outperforming standard augmentation baselines and existing external-data-free augmentation approaches. These results suggest that analytic interference patterns provide a practical and efficient alternative to data-driven generative augmentation methods.

Related papers

• DiffusionVS: A Generative Framework for Robust Visual Servoing Based on Diffusion Policy [10.755467302335617]
  Visual servoing is a fundamental technique in robotic manipulation and navigation.<n> Diffusion Policy maintains temporal consistency by predicting action sequences and improves robustness through implicit data augmentation.<n>Online training paradigm is adopted, continuously expanding the diversity of training data through interactive experience collection.
  arXiv  Detail & Related papers  (2026-06-17T08:06:05Z)
• Prototype-Guided Diffusion: Visual Conditioning without External Memory [2.1155908599769764]
  Prototype Diffusion Model integrates prototype learning directly into the diffusion process for efficient visual conditioning - without external memory.<n>PDM maintains high generation quality while reducing computational and storage overhead, offering a scalable alternative to retrieval-based conditioning in diffusion models.
  arXiv  Detail & Related papers  (2025-08-13T16:18:35Z)
• Generative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution [37.60572296105984]
  We propose a novel diffusion-based generative modeling framework tailored for generative image compression.<n>A reverse neural network is trained to reconstruct images by reversing the compression process directly.<n>This approach achieves smooth rate adjustment and photo-realistic reconstructions with only a minimal number of sampling steps.
  arXiv  Detail & Related papers  (2025-05-27T10:18:24Z)
• Diffusing Differentiable Representations [60.72992910766525]
  We introduce a novel, training-free method for sampling differentiable representations (diffreps) using pretrained diffusion models.<n>We identify an implicit constraint on the samples induced by the diffrep and demonstrate that addressing this constraint significantly improves the consistency and detail of the generated objects.
  arXiv  Detail & Related papers  (2024-12-09T20:42:58Z)
• Oscillation Inversion: Understand the structure of Large Flow Model through the Lens of Inversion Method [60.88467353578118]
  We show that a fixed-point-inspired iterative approach to invert real-world images does not achieve convergence, instead oscillating between distinct clusters. We introduce a simple and fast distribution transfer technique that facilitates image enhancement, stroke-based recoloring, as well as visual prompt-guided image editing.
  arXiv  Detail & Related papers  (2024-11-17T17:45:37Z)
• Scaling Rectified Flow Transformers for High-Resolution Image Synthesis [22.11487736315616]
  Rectified flow is a recent generative model formulation that connects data and noise in a straight line. We improve existing noise sampling techniques for training rectified flow models by biasing them towards perceptually relevant scales. We present a novel transformer-based architecture for text-to-image generation that uses separate weights for the two modalities.
  arXiv  Detail & Related papers  (2024-03-05T18:45:39Z)
• Blue noise for diffusion models [50.99852321110366]
  We introduce a novel and general class of diffusion models taking correlated noise within and across images into account. Our framework allows introducing correlation across images within a single mini-batch to improve gradient flow. We perform both qualitative and quantitative evaluations on a variety of datasets using our method.
  arXiv  Detail & Related papers  (2024-02-07T14:59:25Z)
• ExposureDiffusion: Learning to Expose for Low-light Image Enhancement [87.08496758469835]
  This work addresses the issue by seamlessly integrating a diffusion model with a physics-based exposure model. Our method obtains significantly improved performance and reduced inference time compared with vanilla diffusion models. The proposed framework can work with both real-paired datasets, SOTA noise models, and different backbone networks.
  arXiv  Detail & Related papers  (2023-07-15T04:48:35Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• Reflected Diffusion Models [93.26107023470979]
  We present Reflected Diffusion Models, which reverse a reflected differential equation evolving on the support of the data. Our approach learns the score function through a generalized score matching loss and extends key components of standard diffusion models.
  arXiv  Detail & Related papers  (2023-04-10T17:54:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.