REPARAMETERIZATION HEAD FOR EFFICIENT MULTI-INPUT NETWORKS

Reparameterization techniques have demonstrated their efficacy in improving the efficiency of deep neural networks. However, their application has been largely confined to single-input network structures, leaving multi-input ones, commonly encountered in real-world applications, largely unexplored. In this paper, we formulate reparameterization head (RepHead), the first framework designed to introduce reparameterization into multi-input neural networks. RepHead compresses multiple inputs into a single input and employs reconstruction operations to recover them, thereby transforming multi-input networks into single-input, multi-branch architectures, thereby enabling the application of reparameterization. We demonstrate the usage of RepHead in both image and point cloud domains. Extensive experimental results validate that the integration of RepHead substantially reduces computational overhead and memory requirements while maintaining minimal performance loss.