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The normalizing layer has become one of the basic configurations of deeplearning models, but it still suffers from computational inefficiency,interpretability difficulties, and low generality. After gaining a deeperunderstanding of the recent normalization and normalizer-free research worksfrom a sample's perspective, we reveal the fact that the problem lies in thesampling noise and the inappropriate prior assumption. In this paper, wepropose a simple and effective alternative to normalization, which is called"NoMorelization". NoMorelization is composed of two trainable scalars and azero-centered noise injector. Experimental results demonstrate thatNoMorelization is a general component for deep learning and is suitable fordifferent model paradigms (e.g., convolution-based and attention-based models)to tackle different tasks (e.g., discriminative and generative tasks). Comparedwith existing mainstream normalizers (e.g., BN, LN, and IN) andstate-of-the-art normalizer-free methods, NoMorelization shows the bestspeed-accuracy trade-off.

NoMorelization: Building Normalizer-Free Models from a Sample's Perspective