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Model compression methods can reduce model complexity on the premise ofmaintaining acceptable performance, and thus promote the application of deepneural networks under resource constrained environments. Despite their greatsuccess, the selection of suitable compression methods and design of details ofthe compression scheme are difficult, requiring lots of domain knowledge assupport, which is not friendly to non-expert users. To make more users easilyaccess to the model compression scheme that best meet their needs, in thispaper, we propose AutoMC, an effective automatic tool for model compression.AutoMC builds the domain knowledge on model compression to deeply understandthe characteristics and advantages of each compression method under differentsettings. In addition, it presents a progressive search strategy to efficientlyexplore pareto optimal compression scheme according to the learned priorknowledge combined with the historical evaluation information. Extensiveexperimental results show that AutoMC can provide satisfying compressionschemes within short time, demonstrating the effectiveness of AutoMC.

AutoMC: Automated Model Compression based on Domain Knowledge and Progressive search strategy