Mixed Sampling

Mixed sampling techniques in machine learning aim to improve model generalization and robustness by strategically combining data from different sources or domains. Current research focuses on developing sophisticated sampling strategies, often integrated with generative models (like GANs) or employing techniques like mixup and its variants, to create synthetic data that bridges domain gaps or enhances the informativeness of existing datasets. These methods are proving valuable in diverse applications, including image segmentation, object detection, and cognitive diagnosis, by mitigating the negative effects of data distribution shifts and improving model performance in challenging, real-world scenarios.