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Yes, they do. This work investigates a perspective for deep learning: whetherdifferent normalization layers in a ConvNet require different normalizers. Thisis the first step towards understanding this phenomenon. We allow eachconvolutional layer to be stacked before a switchable normalization (SN) thatlearns to choose a normalizer from a pool of normalization methods. Throughsystematic experiments in ImageNet, COCO, Cityscapes, and ADE20K, we answerthree questions: (a) Is it useful to allow each normalization layer to selectits own normalizer? (b) What impacts the choices of normalizers? (c) Dodifferent tasks and datasets prefer different normalizers? Our results suggestthat (1) using distinct normalizers improves both learning and generalizationof a ConvNet; (2) the choices of normalizers are more related to depth andbatch size, but less relevant to parameter initialization, learning rate decay,and solver; (3) different tasks and datasets have different behaviors whenlearning to select normalizers.

Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?