Learning Grouped Convolution for Efficient Domain Adaptation
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This paper presents Dokei, an effective supervised domain adaptation method to transform a pre-trained CNN model to one involving efficient grouped convolution. The basis of this approach is formalised as a novel optimisation problem constrained by group sparsity pattern (GSP), and a practical solution based on structured regularisation and maximal bipartite matching is provided. We show that it is vital to keep the connections specified by GSP when mapping pre-trained weights to grouped convolution. We evaluate Dokei on various domains and hardware platforms to demonstrate its effectiveness. The models resulting from Dokei are shown to be more accurate and slimmer than prior work targeting grouped convolution, and more regular and easier to deploy than other pruning techniques.
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