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Data augmentation is a widely used technique for enhancing the generalizationability of convolutional neural networks (CNNs) in image classification tasks.Occlusion is a critical factor that affects on the generalization ability ofimage classification models. In order to generate new samples, existing dataaugmentation methods based on information deletion simulate occluded samples byrandomly removing some areas in the images. However, those methods cannotdelete areas of the images according to their structural features of theimages. To solve those problems, we propose a novel data augmentation method,AdvMask, for image classification tasks. Instead of randomly removing areas inthe images, AdvMask obtains the key points that have the greatest influence onthe classification results via an end-to-end sparse adversarial attack module.Therefore, we can find the most sensitive points of the classification resultswithout considering the diversity of various image appearance and shapes of theobject of interest. In addition, a data augmentation module is employed togenerate structured masks based on the key points, thus forcing the CNNclassification models to seek other relevant content when the mostdiscriminative content is hidden. AdvMask can effectively improve theperformance of classification models in the testing process. The experimentalresults on various datasets and CNN models verify that the proposed methodoutperforms other previous data augmentation methods in image classificationtasks.

AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification