EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning

Understand

Finding out the computational redundant part of a trained Deep Neural Network (DNN) is the key question that pruning algorithms target on.

• Many algorithms try to predict model performance of the pruned sub-nets by introducing various evaluation methods.
• But they are either inaccurate or very complicated for general application.
• In this work, we present a pruning method called EagleEye, in which a simple yet efficient evaluation component based on adaptive batch normalization is applied to unveil a strong correlation between different pruned DNN structures and their final settled accuracy.