ShapeBlend: Boosting out-of-distribution robustness in image classification via shape-based blending augmentation
Year: 2026
Type: article
Abstract: Deep Neural Networks (DNNs) often struggle to generalize beyond their training distributions, making them vulnerable to domain shifts. To enhance robustness, various approaches have been developed, particularly focusing on data-centric methods. Modifying the training data can increase diversity and improve generalization, while also introducing bias that positively guides the model’s decision-making. Prior research suggests that DNNs tend to overemphasize texture-based patterns, at the expense of more robust shape-based representations. We introduce ShapeBlend, a novel data augmentation technique that emphasizes image contours, and hence shape features. It blends a contour map from the push-pull CORF operator with the original image at varying strengths. ShapeBlend consistently outperforms state-of-the-art methods across major Out-of-Distribution (OOD) benchmarks (ImageNet-A, ImageNet-R, ImageNet-C, and ImageNet- C ¯ ), setting new records in robustness. Moreover, ShapeBlend’s versatility allows its application during inference. To fully leverage ShapeBlend, we propose Shape-Enhanced Voting (SEV), an inference strategy that aggregates predictions from multiple ShapeBlend-processed images. The combination of ShapeBlend and SEV further enhances domain robustness, with performance gains varying based on the chosen configuration. • We introduce ShapeBlend: a shape-based augmentation for OOD robustness. • ShapeBlend improves performance across multiple robustness benchmarks. • Inference performance is boosted with Shape-enhanced Voting (SEV). • ShapeBlend is compatible with existing pipelines like AugMix and DeepAugment. • A simple, explainable method with strong theoretical motivation.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,090
OpenAlex ID: W7154704782