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Deep convolutional neural networks (CNN) have achieved the unwaveringconfidence in its performance on image processing tasks. The CNN architectureconstitutes a variety of different types of layers including the convolutionlayer and the max-pooling layer. CNN practitioners widely understand the factthat the stability of learning depends on how to initialize the modelparameters in each layer. Nowadays, no one doubts that the de facto standardscheme for initialization is the so-called Kaiming initialization that has beendeveloped by He et al. The Kaiming scheme was derived from a much simpler modelthan the currently used CNN structure having evolved since the emergence of theKaiming scheme. The Kaiming model consists only of the convolution and fullyconnected layers, ignoring the max-pooling layer and the global average poolinglayer. In this study, we derived the initialization scheme again not from thesimplified Kaiming model, but precisely from the modern CNN architectures, andempirically investigated how the new initialization method performs compared tothe de facto standard ones that are widely used today.

Adaptive Signal Variances: CNN Initialization Through Modern Architectures