Modality Selection

Modality selection in machine learning focuses on optimally choosing which data sources (modalities, e.g., visual, audio, tactile) to utilize for improved model performance and efficiency. Current research emphasizes developing algorithms that dynamically select modalities based on factors like data quality, computational constraints, and the relative importance of each modality, often employing techniques such as Shapley value analysis, reinforcement learning (e.g., DDPG), and submodular optimization. This is crucial for applications like federated learning, cross-modal retrieval, and resource-constrained environments, where efficient use of diverse data is paramount for both accuracy and scalability. The impact extends to various fields, including robotics, medical imaging, and IoT, by enabling more robust, efficient, and explainable AI systems.