Manifold Mixup

Manifold Mixup is a data augmentation technique that improves the robustness and generalization of machine learning models, particularly in scenarios with limited labeled data or noisy labels. Current research focuses on adapting Mixup to various data types, including graphs, point clouds, and biological networks, often incorporating geometric or topological information to guide the interpolation process. These advancements aim to enhance model performance in challenging situations, such as cross-lingual transfer learning and open intent classification, leading to more reliable and accurate predictions across diverse applications. The resulting improved model generalization and calibration are significant contributions to machine learning practice.