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.

我们提出了一种基于两个神经网络的等价评估的修复压缩前馈神经网络的方法，通过计算两个神经网络之间的输出差异来刻画由压缩过程产生的输出差异，并使用这个差异来初始化一个新的训练集以减小神经网络间的差异并提高压缩网络的性能，然后通过重新训练该训练集来修复压缩前馈神经网络，并在 MNIST 数据集上应用我们提出的修复方法来展示其有效性和优势。