title: Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes
abstract: Normalization techniques have only recently begun to be exploited in supervised learning tasks. Batch normalization exploits mini-batch statistics to normalize the activations. This was shown to speed up training and result in better models. However its success has been very limited when dealing with recurrent neural networks. On the other hand, layer normalization normalizes the activations across all activities within a layer. This was shown to work  well in the recurrent setting. In this paper we propose a unified view of  normalization techniques, as forms of divisive normalization, which includes layer and batch normalization as special cases. Our second contribution is the finding that a small modification to these normalization schemes, in conjunction with a sparse regularizer on the activations, leads to significant benefits over standard normalization techniques. We demonstrate the effectiveness of our unified divisive normalization framework  in the context of convolutional neural nets and recurrent neural networks, showing  improvements over baselines in image classification, language modeling as well as super-resolution.  
authors: Mengye Ren Renjie Liao Raquel Urtasun Fabian H. Sinz Richard S. Zemel
hIndex: Raquel Urtasun 92