What Not to Erase: Saliency-Constrained Augmentation for Low-Resource Handwritten Character Recognition
Read the original on arXiv Machine Learning →The paper investigates saliency-guided data augmentation for low-resource handwritten character recognition, proposing that saliency maps can be used to design augmentation variants that perturb, preserve, or constrain visually informative regions. It introduces the Explainability Consistency Index (ECI) to measure saliency stability under perturbations and compares classical transformations, random lacunae, and saliency-constrained lacunae. Results show that saliency-constrained augmentation can improve recognition and explanation consistency, but the optimal strategy depends on architecture and corpus, suggesting saliency should be used as a control mechanism rather than a fixed augmentation rule.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.