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Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?
[article]
2018
arXiv
pre-print
Yes, they do. This work investigates a perspective for deep learning: whether different normalization layers in a ConvNet require different normalizers. This is the first step towards understanding this phenomenon. We allow each convolutional layer to be stacked before a switchable normalization (SN) that learns to choose a normalizer from a pool of normalization methods. Through systematic experiments in ImageNet, COCO, Cityscapes, and ADE20K, we answer three questions: (a) Is it useful to
arXiv:1811.07727v1
fatcat:4uzmszzmgzgsnh7a6y7lzzymaa