Expert Augmentation

Expert augmentation enhances machine learning models by incorporating expert knowledge or data to improve performance and generalization. Current research focuses on augmenting data using various methods, including counterfactual reasoning, guided augmentations based on domain-specific knowledge (e.g., geometric models for satellite docking, sentence structure for NLP), and leveraging expert models to generate synthetic data that extends the training distribution. This approach addresses challenges like data scarcity in specialized domains and improves model robustness and generalization capabilities across diverse applications, including robotics, natural language processing, and 3D point cloud processing.