  Channel pruning is widely used to reduce the complexity of deep network
models. Recent pruning methods usually identify which parts of the network to
discard by proposing a channel importance criterion. However, recent studies
have shown that these criteria do not work well in all conditions. In this
paper, we propose a novel Feature Shift Minimization (FSM) method to compress
CNN models, which evaluates the feature shift by converging the information of
both features and filters. Specifically, we first investigate the compression
efficiency with some prevalent methods in different layer-depths and then
propose the feature shift concept. Then, we introduce an approximation method
to estimate the magnitude of the feature shift, since it is difficult to
compute it directly. Besides, we present a distribution-optimization algorithm
to compensate for the accuracy loss and improve the network compression
efficiency. The proposed method yields state-of-the-art performance on various
benchmark networks and datasets, verified by extensive experiments. Our codes
are available at: https://github.com/lscgx/FSM.
