论文标题
AutoMC：基于域知识和渐进搜索策略的自动化模型压缩
AutoMC: Automated Model Compression based on Domain Knowledge and Progressive search strategy
论文作者
论文摘要
模型压缩方法可以在维持可接受的性能的前提下降低模型的复杂性，从而促进在资源约束环境中深层神经网络的应用。尽管他们取得了巨大的成功，但很难选择合适的压缩方法和压缩方案细节的设计，这需要大量的领域知识作为支持，这对非专家用户不友好。为了使更多用户轻松访问最能满足其需求的模型压缩方案，我们建议AutoMC，这是一种有效的自动压缩工具。 AUTOMC在模型压缩上构建了域知识，以深入了解不同设置下每种压缩方法的特征和优势。此外，它提出了一种渐进式搜索策略，可以根据所学的先验知识与历史评估信息有效探索帕累托最佳压缩方案。广泛的实验结果表明，AutoMC可以在短时间内提供令人满意的压缩方案，这证明了AutoMC的有效性。
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.
