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Optimizing parameters with momentum, normalizing data values, and usingrectified linear units (ReLUs) are popular choices in neural network (NN)regression. Although ReLUs are popular, they can collapse to a constantfunction and "die", effectively removing their contribution from the model.While some mitigations are known, the underlying reasons of ReLUs dying duringoptimization are currently poorly understood. In this paper, we consider theeffects of target normalization and momentum on dying ReLUs. We findempirically that unit variance targets are well motivated and that ReLUs diemore easily, when target variance approaches zero. To further investigate thismatter, we analyze a discrete-time linear autonomous system, and showtheoretically how this relates to a model with a single ReLU and how commonproperties can result in dying ReLU. We also analyze the gradients of asingle-ReLU model to identify saddle points and regions corresponding to dyingReLU and how parameters evolve into these regions when momentum is used.Finally, we show empirically that this problem persist, and is aggravated, fordeeper models including residual networks.

The effect of Target Normalization and Momentum on Dying ReLU