An innovative contrastive learning approach to improve image recognition robustness and interpretability via simulated environmental perturbations

In the field of pattern recognition, the noise inherent in real-world images poses a significant challenge to traditional image processing methodologies. While existing approaches have made progress in addressing this issue, they often struggle with limited model generalization, data distribution shifts, and domain adaptability discrepancies between simulated environments and real-world contexts, compromising efficiency and robustness. In this paper, we propose a novel contrastive learning strategy for Enhancing Robustness and Interpretability in Image Recognition through Environmental Perturbations (ERIEP) of clear-featured image data. ERIEP meticulously identifies a set of core visual features, termed “invariant features”, which can offer optimal explanations for image predictions. Concurrently, it emphasizes learning noise-resistant strategies to amplify the model’s interpretability. Through ERIEP’s contrastive learning approach, we address complex images, enabling the model to progressively refine its understanding of both the invariant features and noise mitigation technique. Our extensive experiments on CIFAR-10, CIFAR-100, and ImageNet-1K demonstrate that ERIEP significantly outperforms several state-of-the-art image-processing baselines, showing robust performance under various noise intensities and environmental perturbations.