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Modern pattern recognition methods are based on convolutional networks sincethey are able to learn complex patterns that benefit the classification.However, convolutional networks are computationally expensive and require aconsiderable amount of memory, which limits their deployment on low-power andresource-constrained systems. To handle these problems, recent approaches haveproposed pruning strategies that find and remove unimportant neurons (i.e.,filters) in these networks. Despite achieving remarkable results, existingpruning approaches are ineffective since the accuracy of the original networkis degraded. In this work, we propose a novel approach to efficiently removefilters from convolutional networks. Our approach estimates the filterimportance based on its relationship with the class label on a low-dimensionalspace. This relationship is computed using Partial Least Squares (PLS) andVariable Importance in Projection (VIP). Our method is able to reduce up to 67%of the floating point operations (FLOPs) without penalizing the networkaccuracy. With a negligible drop in accuracy, we can reduce up to 90% of FLOPs.Additionally, sometimes the method is even able to improve the accuracycompared to original, unpruned, network. We show that employing PLS+VIP as thecriterion for detecting the filters to be removed is better than recent featureselection techniques, which have been employed by state-of-the-art pruningmethods. Finally, we show that the proposed method achieves the highest FLOPsreduction and the smallest drop in accuracy when compared to state-of-the-artpruning approaches. Codes are available at:https://github.com/arturjordao/PruningNeuralNetworks

Pruning Deep Neural Networks using Partial Least Squares