Domain Augmentation

Domain augmentation is a technique used to improve the generalization ability of machine learning models by enriching training data with synthetic or modified samples from different domains. Current research focuses on developing effective augmentation strategies tailored to specific tasks and data modalities, including methods that leverage large language models for novel domain generation and those employing adversarial or mixing techniques to align features across domains. These advancements are crucial for addressing challenges like domain shift in various applications, such as 3D object detection, digital pathology, and crowd counting, ultimately leading to more robust and reliable models in real-world scenarios.