# MUXConv Information Multiplexing in Convolutional Neural Networks

Convolutional neural networks have witnessed remarkable improvements in computational efficiency in recent years . The price of the efficiency is the sub-optimal flow of information across space and channels in the network . To overcome this limitation, we present MUXConv, a layer that is designed to increase the flow of . information by progressively multiplexing channel and spatial information . The resulting models, dubbed MUXNets, match the performance (75.3% top-1 accuracy) and multiply-add operations of MobileNetV3 while being . 1.6$\times$ more compact, and outperform other mobile models in all three criteria . MUXNet also performs well under transfer learning and when adapted to object detection .