SGD-Mix: Enhancing Domain-Specific Image Classification with Label-Preserving Data Augmentation

Effective data augmentation for domain-specific image classification must balance three competing objectives: diversity, faithfulness, and label clarity. However, current methods, including state-of-the-art diffusion models, struggle to achieve this balance and are further limited by issues such as stochastic outputs under strong transformations. We propose SGD-Mix, a novel framework that systematically reconciles these objectives. Our approach employs saliency-guided mixing to preserve foreground semantics while introducing diverse backgrounds, followed by a domain-specific fine-tuned diffusion model that refines the output to ensure high fidelity and strict label consistency. Extensive experiments across fine-grained, long-tail, few-shot, and background robustness tasks demonstrate that SGD-Mix achieves state-of-the-art performance, surpassing existing diffusion-based and non-generative methods by notable margins. Our code is available at https://github.com/RadonDong/sgdmix