  Model compression methods can reduce model complexity on the premise of
maintaining acceptable performance, and thus promote the application of deep
neural networks under resource constrained environments. Despite their great
success, the selection of suitable compression methods and design of details of
the compression scheme are difficult, requiring lots of domain knowledge as
support, which is not friendly to non-expert users. To make more users easily
access to the model compression scheme that best meet their needs, in this
paper, we propose AutoMC, an effective automatic tool for model compression.
AutoMC builds the domain knowledge on model compression to deeply understand
the characteristics and advantages of each compression method under different
settings. In addition, it presents a progressive search strategy to efficiently
explore pareto optimal compression scheme according to the learned prior
knowledge combined with the historical evaluation information. Extensive
experimental results show that AutoMC can provide satisfying compression
schemes within short time, demonstrating the effectiveness of AutoMC.
