Suppress and Diversify: Refining Robust Pathways for Corruption Robustness

2026-08-06

AI Summary

This work addresses the insufficient robustness of current models under natural image corruptions, particularly their vulnerability in safety-critical scenarios. The authors present the first explicit characterization of internal robust computational pathways within neural networks, revealing a consistent attenuation of robust features across layers. To counteract this degradation, they propose a novel “Suppress and Diversify” mechanism that is architecture-agnostic, parameter-free, and incurs zero overhead at test time. This approach dynamically selects and diversifies symmetry-preserving robust pathways to enhance overall model robustness. Extensive experiments across eight benchmarks demonstrate that the method consistently improves performance across diverse vision tasks, backbone architectures, and complex real-world conditions, highlighting its strong generalizability and scalability.

Abstract

Model robustness against natural image corruptions is essential for safety-critical applications. While existing methods primarily focus on implicit representation learning, we provide the first systematic exploration of computational pathways to explicitly characterize internal robustness. We identify a progressive decay of robust features across network layers and establish a functional dependency between the prevalence of these features and model performance. To exploit these insights, we propose Suppress and Diversify (S\&D), a non-intrusive refinement approach that enhances robustness by dynamically selecting robust pathways and diversifying them through symmetry-preserving transformations. S\&D is architecture-agnostic, parameter-free, and incurs zero test-time overhead. Extensive evaluations across eight benchmarks demonstrate that S\&D consistently improves performance across multiple vision tasks, diverse backbones, and complex real-world scenarios, highlighting its broad efficacy and scalability.

Problem

• corruption robustness
• model robustness
• natural image corruptions
• robust pathways
• internal robustness

Innovation

• corruption robustness
• computational pathways
• feature diversification
• symmetry-preserving transformations
• parameter-free refinement