AI RESEARCH
Why CNN Features Are not Gaussian: A Statistical Anatomy of Deep Representations
arXiv CS.LG
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ArXi:2411.05183v4 Announce Type: replace-cross Deep convolutional neural networks (CNNs) are commonly analyzed through geometric and linear-algebraic perspectives, yet the statistical distribution of their internal feature activations remains poorly understood. In many applications, deep features are implicitly treated as Gaussian when modeling densities. In this work, we empirically examine this assumption and show that it does not accurately describe the distribution of CNN feature activations.