Compression Repair for Feedforward Neural Networks Based on Model
  Equivalence Evaluation
Zihao Mo Yejiang Yang Shuaizheng Lu Weiming Xiang
In this paper, we propose a method of repairing compressed Feedforward Neural
Networks (FNNs) based on equivalence evaluation of two neural networks. In the
repairing framework, a novel neural network equivalence evaluation method is
developed to compute the output discrepancy between two neural networks. The
output discrepancy can quantitatively characterize the output difference
produced by compression procedures. Based on the computed output discrepancy,
the repairing method first initializes a new training set for the compressed
networks to narrow down the discrepancy between the two neural networks and
improve the performance of the compressed network. Then, we repair the
compressed FNN by re-training based on the training set. We apply our developed
method to the MNIST dataset to demonstrate the effectiveness and advantages of
our proposed repair method.