Unseen Data

Unseen data, encompassing data from previously unencountered distributions or tasks, poses a significant challenge for machine learning models. Current research focuses on improving model generalizability to unseen data through techniques like continual learning, domain generalization, and the development of foundation models adaptable to diverse data types and tasks, often leveraging architectures such as transformers, generative adversarial networks, and graph neural networks. Addressing this challenge is crucial for deploying reliable AI systems in real-world applications, particularly in sensitive domains like healthcare and security, where robustness to unexpected inputs is paramount.