  Deep Convolutional Neural Networks (CNN) has achieved significant success in
computer vision field. However, the high computational cost of the deep complex
models prevents the deployment on edge devices with limited memory and
computational resource. In this paper, we proposed a novel filter pruning for
convolutional neural networks compression, namely spectral clustering filter
pruning with soft self-adaption manners (SCSP). We first apply spectral
clustering on filters layer by layer to explore their intrinsic connections and
only count on efficient groups. By self-adaption manners, the pruning
operations can be done in few epochs to let the network gradually choose
meaningful groups. According to this strategy, we not only achieve model
compression while keeping considerable performance, but also find a novel angle
to interpret the model compression process.
