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Skip connection, is a widely-used technique to improve the performance andthe convergence of deep neural networks, which is believed to relieve thedifficulty in optimization due to non-linearity by propagating a linearcomponent through the neural network layers. However, from another point ofview, it can also be seen as a modulating mechanism between the input and theoutput, with the input scaled by a pre-defined value one. In this work, weinvestigate how the scale factors in the effectiveness of the skip connectionand reveal that a trivial adjustment of the scale will lead to spuriousgradient exploding or vanishing in line with the deepness of the models, whichcould be addressed by normalization, in particular, layer normalization, whichinduces consistent improvements over the plain skip connection. Inspired by thefindings, we further propose to adaptively adjust the scale of the input byrecursively applying skip connection with layer normalization, which promotesthe performance substantially and generalizes well across diverse tasksincluding both machine translation and image classification datasets.

Rethinking Skip Connection with Layer Normalization in Transformers and ResNets