  To deploy a well-trained CNN model on low-end computation edge devices, it is
usually supposed to compress or prune the model under certain computation
budget (e.g., FLOPs). Current filter pruning methods mainly leverage feature
maps to generate important scores for filters and prune those with smaller
scores, which ignores the variance of input batches to the difference in sparse
structure over filters. In this paper, we propose a data agnostic filter
pruning method that uses an auxiliary network named Dagger module to induce
pruning and takes pretrained weights as input to learn the importance of each
filter. In addition, to help prune filters with certain FLOPs constraints, we
leverage an explicit FLOPs-aware regularization to directly promote pruning
filters toward target FLOPs. Extensive experimental results on CIFAR-10 and
ImageNet datasets indicate our superiority to other state-of-the-art filter
pruning methods. For example, our 50\% FLOPs ResNet-50 can achieve 76.1\% Top-1
accuracy on ImageNet dataset, surpassing many other filter pruning methods.
