Abstract. Convolutional neural networks (CNNs) have dramatically
advanced the state-of-art in a number of domains. However, most models
are both computation and memory intensive, which arouse the interest
of network compression. While existing compression methods achieve
good performance, they suffer from three limitations: 1) the inevitable
retraining with enormous labeled data; 2) the massive GPU hours for
retraining; 3) the training tricks for model compression. Especially the
requirement of retraining on original datasets makes it difficult to apply
in many real-world scenarios, where training data is not publicly available. In this paper, we reveal that re-normalization is the practical and
effective way to alleviate the above limitations. Through quantization
or pruning, most methods may compress a large number of parameters
but ignore the core role in performance degradation, which is the Gaussian conjugate prior induced by batch normalization. By employing the
re-estimated statistics in batch normalization, we significantly improve
the accuracy of compressed CNNs. Extensive experiments on ImageNet
show it outperforms baselines by a large margin and is comparable to
label-based methods. Besides, the fine-tuning process takes less than 5
minutes on CPU, using 1000 unlabeled images