Multi Domain Evaluation

Multi-domain evaluation assesses the performance of machine learning models across diverse datasets and application areas, aiming to improve generalization and robustness beyond single-domain benchmarks. Current research focuses on developing evaluation frameworks and benchmarks that encompass a wider range of domains, including image processing (e.g., super-resolution, geolocalization, semantic segmentation), natural language processing (e.g., dialogue evaluation, document understanding), and exploring techniques like self-training and geometric disentanglement to enhance model performance across these domains. This work is crucial for building more reliable and adaptable AI systems, ultimately leading to more impactful applications in various fields.