Stronger Computational Separations Between Multimodal and Unimodal Machine Learning

There exist average-case computational separations between multimodal and unimodal machine learning tasks, where multimodal learning is feasible in polynomial time but the corresponding unimodal task is computationally hard. However, any such separation implies the existence of cryptographic key agreement protocols, suggesting that very strong computational advantages of multimodal learning may arise infrequently in practice.