SGD-Mix: Enhancing Domain-Specific Image Classification with Label-Preserving Data Augmentation
This addresses the challenge of generating effective augmented data for domain-specific image classification, which is incremental as it builds on existing diffusion model methods.
The paper tackled the problem of data augmentation for domain-specific image classification by proposing a framework that integrates diversity, faithfulness, and label clarity, resulting in superior performance across various tasks.
Data augmentation for domain-specific image classification tasks often struggles to simultaneously address diversity, faithfulness, and label clarity of generated data, leading to suboptimal performance in downstream tasks. While existing generative diffusion model-based methods aim to enhance augmentation, they fail to cohesively tackle these three critical aspects and often overlook intrinsic challenges of diffusion models, such as sensitivity to model characteristics and stochasticity under strong transformations. In this paper, we propose a novel framework that explicitly integrates diversity, faithfulness, and label clarity into the augmentation process. Our approach employs saliency-guided mixing and a fine-tuned diffusion model to preserve foreground semantics, enrich background diversity, and ensure label consistency, while mitigating diffusion model limitations. Extensive experiments across fine-grained, long-tail, few-shot, and background robustness tasks demonstrate our method's superior performance over state-of-the-art approaches.