Diffusemix: Label-Preserving Data Augmentation with Diffusion Models

Recently, a number of image-mixing-based augmentation techniques have been introduced to improve the gen-eralization of deep neural networks. In these techniques, two or more randomly selected natural images are mixed together to generate an augmented image. Such methods may not only omit important portions of the input images but also introduce label ambiguities by mixing images across labels resulting in misleading supervisory signals. To address these limitations, we propose Diffusemix, a novel data augmentation technique that leverages a diffusion model to reshape training images, supervised by our bespoke conditional prompts. First, concatenation of a partial natural image and its generated counterpart is ob-tained which helps in avoiding the generation of unrealistic images or label ambiguities. Then, to enhance resilience against adversarial attacks and improves safety measures, a randomly selected structural pattern from a set of frac-tal images is blended into the concatenated image to form the final augmented image for training. Our empirical results on seven different datasets reveal that Diffusemix achieves superior performance compared to existing state-of-the-art methods on tasks including general classification, fine- grained classification, fine-tuning, data scarcity, and adversarial robustness. Augmented datasets and codes are available here: https://diffusemix.github.io/