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Although the facial makeup transfer network has achieved high-qualityperformance in generating perceptually pleasing makeup images, its capabilityis still restricted by the massive computation and storage of the networkarchitecture. We address this issue by compressing facial makeup transfernetworks with collaborative distillation and kernel decomposition. The mainidea of collaborative distillation is underpinned by a finding that theencoder-decoder pairs construct an exclusive collaborative relationship, whichis regarded as a new kind of knowledge for low-level vision tasks. For kerneldecomposition, we apply the depth-wise separation of convolutional kernels tobuild a light-weighted Convolutional Neural Network (CNN) from the originalnetwork. Extensive experiments show the effectiveness of the compression methodwhen applied to the state-of-the-art facial makeup transfer network --BeautyGAN.

Compressing Facial Makeup Transfer Networks by Collaborative Distillation and Kernel Decomposition