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We propose a simple extension to the ReLU-family of activation functions thatallows them to shift the mean activation across a layer towards zero. Combinedwith proper weight initialization, this alleviates the need for normalizationlayers. We explore the training of deep vanilla recurrent neural networks(RNNs) with up to 144 layers, and show that bipolar activation functions helplearning in this setting. On the Penn Treebank and Text8 language modelingtasks we obtain competitive results, improving on the best reported results fornon-gated networks. In experiments with convolutional neural networks withoutbatch normalization, we find that bipolar activations produce a faster drop intraining error, and results in a lower test error on the CIFAR-10classification task.

Shifting Mean Activation Towards Zero with Bipolar Activation Functions