# Context-guided Responsible Data Augmentation with Diffusion Models Source: [Context-guided Responsible Data Augmentation with Diffusion Models]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:17:49.742Z Authors: Khawar Islam, Naveed Akhtar Published: 2025-03-12T00:12:27.000Z ## Abstract Generative diffusion models offer a natural choice for data augmentation when training complex vision models. However, ensuring reliability of their generative content as augmentation samples remains an open challenge. Despite a number of techniques utilizing generative images to strengthen model training, it remains unclear how to utilize the combination of natural and generative images as a rich supervisory signal for effective model induction. In this regard, we propose a text-to-image (T2I) data augmentation method, named DiffCoRe-Mix, that computes a set of generative counterparts for a training sample with an explicitly constrained diffusion model that leverages sample-based context and negative prompting for a reliable augmentation sample generation. To preserve key semantic axes, we also filter out undesired generative samples in our augmentation process. To that end, we propose a hard-cosine filtration in the embedding space of CLIP. Our approach systematically mixes the natural and generative images at pixel and patch levels. We extensively evaluate our technique on ImageNet-1K,Tiny ImageNet-200, CIFAR-100, Flowers102, CUB-Birds, Stanford Cars, and Caltech datasets, demonstrating a notable increase in performance across the board, achieving up to $\\sim 3\\%$ absolute gain for top-1 accuracy over the state-of-the-art methods, while showing comparable computational overhead. Our code is publicly available at https://github.com/khawar-islam/DiffCoRe-Mix ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()