Domain Robustness

Domain robustness in machine learning focuses on developing models that perform reliably across diverse datasets and conditions, overcoming limitations of models trained and tested on similar data. Current research emphasizes improving model robustness through techniques like adversarial training, meta-learning, and the use of synthetic data augmentation, often applied to deep learning architectures such as transformers. This work is crucial for deploying reliable AI systems in real-world applications, particularly in high-stakes domains like healthcare and cybersecurity, where model failures can have significant consequences. Improved domain robustness is essential for building trustworthy and generalizable AI systems.