Domain Generalization techniques aim to enhance model robustness by simulating novel data distributions during training, typically through various augmentation or stylization strategies. However, these methods frequently suffer from limited control over the diversity of generated images and lack assurance that these images span distinct distributions. To address these challenges, we propose FDS, Feedback-guided Domain Synthesis, a novel strategy that employs diffusion models to synthesize novel, pseudo-domains by training a single model on all source domains and performing domain mixing based on learned features. By incorporating images that pose classification challenges to models trained on original samples, alongside the original dataset, we ensure the generation of a training set that spans a broad distribution spectrum. Our comprehensive evaluations demonstrate that this methodology sets new benchmarks in domain generalization performance across a range of challenging datasets, effectively managing diverse types of domain shifts. The implementation is available at: \url{https://github.com/Mehrdad-Noori/FDS.git}.

翻译：领域泛化技术旨在通过训练过程中模拟新的数据分布来增强模型的鲁棒性，通常采用各种数据增强或风格化策略。然而，这些方法往往难以有效控制生成图像的多样性，且无法保证这些图像覆盖真正不同的分布。为解决这些问题，我们提出了FDS（反馈引导域合成），这是一种新颖的策略，它利用扩散模型合成新的伪域：首先在所有源域上训练单一模型，然后基于学习到的特征进行域混合。通过将那些对原始样本训练的模型构成分类挑战的图像与原始数据集相结合，我们确保生成的训练集能够覆盖更广泛的分布范围。全面的评估结果表明，该方法在一系列具有挑战性的数据集上为领域泛化性能树立了新的基准，并能有效处理多种类型的域偏移。实现代码已公开于：\url{https://github.com/Mehrdad-Noori/FDS.git}。