CNN feature activations follow long-tailed Weibull-like distributions with increasing tail dependence by depth rather than Gaussian, indicating a Matthew process that concentrates signal in tails.
Integrating flexible normalization into mi- dlevel representations of deep convolutional neural networks.Neural computation, 31(11):2138– 2176
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Why CNN Features Are not Gaussian: A Statistical Anatomy of Deep Representations
CNN feature activations follow long-tailed Weibull-like distributions with increasing tail dependence by depth rather than Gaussian, indicating a Matthew process that concentrates signal in tails.