GenMix：利用生成式扩散模型图像编辑实现高效数据增强
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing
AI总结：
本文提出提示引导的生成式数据增强方法 GenMix，通过扩散模型图像编辑、图像融合与分形图案提升域内及跨域分类、自监督学习和对抗鲁棒性。
摘要：
数据增强被广泛用于提升视觉分类任务的泛化能力。然而，当源域与目标域存在差异时（如域适应场景），传统方法因无法解决域间差距而表现不佳。本文提出 GenMix，这是一种可泛化的、由提示引导的生成式数据增强方法，可同时提升域内和跨域图像分类性能。该技术利用图像编辑，依据针对每种问题类型专门设计的自定义条件提示来生成增强图像。通过将输入图像的局部与其经过编辑的生成对应图像相融合，并引入分形图案，该方法缓解了图像不真实和标签模糊的问题，从而提升所得模型的性能与对抗鲁棒性。研究在八个用于通用分类和细粒度分类的公开数据集上，于域内和跨域设置中开展了大量实验，验证了方法的有效性。此外，论文还展示了其在自监督学习、数据稀缺条件下学习以及对抗鲁棒性方面的性能提升。与现有最先进方法相比，该技术在所有方面均取得了更优性能。
英文摘要：
Data augmentation is widely used to enhance generalization in visual classification tasks. However, traditional methods struggle when source and target domains differ, as in domain adaptation, due to their inability to address domain gaps. This paper introduces GenMix, a generalizable prompt-guided generative data augmentation approach that enhances both in-domain and cross-domain image classification. Our technique leverages image editing to generate augmented images based on custom conditional prompts, designed specifically for each problem type. By blending portions of the input image with its edited generative counterpart and incorporating fractal patterns, our approach mitigates unrealistic images and label ambiguity, improving the performance and adversarial robustness of the resulting models. Efficacy of our method is established with extensive experiments on eight public datasets for general and fine-grained classification, in both in-domain and cross-domain settings. Additionally, we demonstrate performance improvements for self-supervised learning, learning with data scarcity, and adversarial robustness. As compared to the existing state-of-the-art methods, our technique achieves stronger performance across the board.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
