The Effectiveness of Noise in Data Augmentation for Fine-Grained Image Classification

Recognizing images from subcategories with subtle differences remains a challenging task due to the scarcity of quantity and diversity of training samples. Existing data augmentation methods either rely on models trained with fully annotated data or involve human in the loop, which is labor-intensive. In this paper, we propose a simple approach that leverages large amounts of noisy images from the Web for fine-grained image classification. Beginning with a deep model taken as input image patches for feature representation, the maximum entropy learning criterion is first introduced to improve the score-based patch selection. Then a noise removal procedure is designed to verify the usefulness of noisy images in the augmented data for classification. Extensive experiments on standard, augmented, and combined datasets with and without noise validate the effectiveness of our method. Generally, we achieve comparable results on benchmark datasets, e.g., CUB-Birds, Stanford Dogs, and Stanford Cars, with only 50 augmented noisy samples for every category.