Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Compressing Facial Makeup Transfer Networks by Collaborative
  Distillation and Kernel Decomposition   Although the facial makeup transfer network has achieved high-quality
performance in generating perceptually pleasing makeup images, its capability
is still restricted by the massive computation and storage of the network
architecture. We address this issue by compressing facial makeup transfer
networks with collaborative distillation and kernel decomposition. The main
idea of collaborative distillation is underpinned by a finding that the
encoder-decoder pairs construct an exclusive collaborative relationship, which
is regarded as a new kind of knowledge for low-level vision tasks. For kernel
decomposition, we apply the depth-wise separation of convolutional kernels to
build a light-weighted Convolutional Neural Network (CNN) from the original
network. Extensive experiments show the effectiveness of the compression method
when applied to the state-of-the-art facial makeup transfer network --
BeautyGAN.

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The categories of this paper are: math-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: math
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
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The categories of this paper are: q-bio