Metric Evaluation

Metric evaluation assesses the performance of algorithms and models, aiming to develop robust and reliable methods for comparing and ranking different approaches. Current research focuses on addressing limitations of existing metrics, such as sensitivity to data variance and the need for more holistic evaluations encompassing multiple aspects beyond simple accuracy (e.g., fairness, efficiency, temporal and spatial aspects in video generation). This work is crucial for advancing various fields, including machine translation, federated learning, and AI model development, by providing more accurate and informative assessments that ultimately lead to improved model design and deployment.