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

SCSP: Spectral Clustering Filter Pruning with Soft Self-adaption Manners