Improving Art Style Classification Through Data Augmentation Using Diffusion Models

Abstract

Classifying pictorial styles in artworks is a complex challenge due to the diversity and lack of available datasets, which often limit the performance of machine learning models. To address this issue, we propose a novel data augmentation approach using Diffusion models in contrast to traditional augmentation techniques. Our method generates new samples based on the existing data, expanding the available dataset and enhancing the generalization capability of classification models. We evaluate the effectiveness of this data augmentation technique by training deep learning models with varying proportions of augmented and real data and assessing their performance in pictorial style classification. Our results demonstrate that the proposed Diffusion model-based augmentation significantly improves classification accuracy, suggesting that it can be a viable solution for overcoming data limitations in similar applications.