PP-Grading: Hierarchical Authorization using Polarization Pruning

With the popularity of neural networks, more and more service providers are providing us with convenient neural network services. In this situation, it is necessary for service providers to grant hierarchical authorization of the networks to meet the service requirements of different users. Pruning technology, as a mainstream method for modifying neural network structures, can also be applied in the hierarchical authorization of neural networks. This article uses the method of polarization pruning to decentralize and grade neural networks. Firstly, we extract the scale factors of the batch normalization(BN) layer as a measure of the importance of each channel. Then, sparse methods are used to screen for scale factors. In order to make the selected scale factors have more obvious and reasonable truncation intervals, we also use polarization methods to induce clustering of scale factors. According to different grading requirements, we prune the channels corresponding to different quantities of scale factors. The experiment proves that our method has good grading performance on different datasets and different neural networks. Moreover, good grading results can be achieved by pruning only a small portion of the channels.