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Input margins can predict generalization too
While margins have been shown to be correlated with the generalization ability of a model when measured at its hidden representations (hidden margins), no such link between large margins and generalization has been established for input margins.
The Missing Margin: How Sample Corruption Affects Distance to the Boundary in ANNs
A global estimate of margin size is usually used in the literature.
Pre-interpolation loss behaviour in neural networks
When training neural networks as classifiers, it is common to observe an increase in average test loss while still maintaining or improving the overall classification accuracy on the same dataset.
DNNs as Layers of Cooperating Classifiers
A robust theoretical framework that can describe and predict the generalization ability of deep neural networks (DNNs) in general circumstances remains elusive.
