Can I read Post-synaptic Potential Regularization Has Potential on EtoBox?

Post-synaptic Potential Regularization Has Potential by Tartaglione, Enzo; Perlo, Daniele; Grangetto, Marco is a scholarly article available to read on EtoBox.

What is Post-synaptic Potential Regularization Has Potential about?

Improving generalization is one of the main challenges for training deep neural networks on classification tasks. In particular, a number of techniques have been proposed, aiming to boost the performance on unseen data: from standard data augmentation techniques to the $\ell_2$ regularization, dropout, batch normalization, entropy-driven SGD and many more.\\ In this work we propose an elegant, simple and principled approach: post-synaptic potential regularization (PSP). We tested this regularization on a number of different state-of-the-art scenarios. Empirical results show that PSP achieves a classification error comparable to more sophisticated learning strategies in the MNIST scenario, while improves the generalization compared to $\ell_2$ regularization in deep architectures trained on CIFAR-10.

Author

Tartaglione, Enzo; Perlo, Daniele; Grangetto, Marco

Published

2019

Language

EN