  Filter pruning is widely adopted to compress and accelerate the Convolutional
Neural Networks (CNNs), but most previous works ignore the relationship between
filters and channels in different layers. Processing each layer independently
fails to utilize the collaborative relationship across layers. In this paper,
we intuitively propose a novel pruning method by explicitly leveraging the
Filters Similarity in Consecutive Layers (FSCL). FSCL compresses models by
pruning filters whose corresponding features are more worthless in the model.
The extensive experiments demonstrate the effectiveness of FSCL, and it yields
remarkable improvement over state-of-the-art on accuracy, FLOPs and parameter
reduction on several benchmark models and datasets.
