Agnostic Metric

Agnostic metrics are evaluation measures designed to assess model performance independently of specific datasets, model architectures, or tasks. Current research focuses on developing such metrics for various applications, including evaluating generative models (e.g., using wavelet transforms), monitoring neural network reliability (e.g., by analyzing mutual information or incorporating threshold selection strategies), and assessing the explainability and multimodality of machine learning models (e.g., using Shapley values). The development of robust agnostic metrics is crucial for ensuring the reliability and trustworthiness of AI systems across diverse domains, facilitating fairer comparisons between models, and ultimately improving the safety and effectiveness of AI applications.