LayerMix: Enhanced Data Augmentation through Fractal Integration for Robust Deep Learning

TL;DR

LayerMix is a novel fractal-based data augmentation technique that improves deep learning model robustness against distribution shifts, adversarial attacks, and corruptions by generating semantically consistent synthetic samples.

Contribution

It introduces a structured fractal integration method for data augmentation that enhances model generalization and robustness beyond traditional random transformations.

Findings

Significantly improves classification accuracy on benchmark datasets.

Enhances model resilience to natural corruptions and adversarial attacks.

Boosts model calibration and prediction consistency.

Abstract

Deep learning models have demonstrated remarkable performance across various computer vision tasks, yet their vulnerability to distribution shifts remains a critical challenge. Despite sophisticated neural network architectures, existing models often struggle to maintain consistent performance when confronted with Out-of-Distribution (OOD) samples, including natural corruptions, adversarial perturbations, and anomalous patterns. We introduce LayerMix, an innovative data augmentation approach that systematically enhances model robustness through structured fractal-based image synthesis. By meticulously integrating structural complexity into training datasets, our method generates semantically consistent synthetic samples that significantly improve neural network generalization capabilities. Unlike traditional augmentation techniques that rely on random transformations, LayerMix employs a structured mixing pipeline that preserves original image semantics while introducing controlled variability. Extensive experiments across multiple benchmark datasets, including CIFAR-10, CIFAR-100, ImageNet-200, and ImageNet-1K demonstrate LayerMixs superior performance in classification accuracy and substantially enhances critical Machine Learning (ML) safety metrics, including resilience to natural image corruptions, robustness against adversarial attacks, improved model calibration and enhanced prediction consistency. LayerMix represents a significant advancement toward developing more reliable and adaptable artificial intelligence systems by addressing the fundamental challenges of deep learning generalization. The code is available at https://github.com/ahmadmughees/layermix.