We present a generalization bound for feedforward neural networks in terms of the product of the spectral norms of the layers and the Frobenius norm of the weights. The generalization bound is derived using a PAC-Bayes analysis.
REF then devises PAC-Bayes bounds based on spectral norms of the layers and the Frobenius norm of the weights of the network.
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
ArXiv
ArXiv
Computer Science Mathematics