Title: Towards Performance-maximizing Network Pruning via Global Channel Attention. Abstract: Network pruning has attracted increasing attention recently for its capability of transferring large-scale neural networks (e.g., CNNs) into resource-constrained devices. Such a transfer is typically achieved by removing redundant network parameters while retaining its generalization performance in a static or dynamic pruning manner. Concretely, static pruning usually maintains a larger and fit-to-all (samples) compressed network by removing the same channels for all samples, while dynamic pruning can adaptively remove (more) different channels for different samples and obtain state-of-the-art performance along with a higher compression ratio. However, since the system has to preserve the complete network information for sample-specific pruning, dynamic pruning methods are usually not memory-efficient. In this paper, our interest is to explore a static alternative, dubbed GlobalPru, to conventional static pruning methods that can take into account both compression ratio and model performance maximization. Specifically, a novel channel attention-based learn-to-rank algorithm is proposed to learn the global channel attention of the network for various samples, wherein, each sample-specific channel saliency is forced to reach an agreement on the global ranking. Hence, all samples can empirically share the same pruning priority of channels to achieve channel pruning with minimal performance loss. Extensive experiments demonstrate that the proposed GlobalPru can achieve better performance than state-of-the-art static and dynamic pruning methods by significant margins.