Post-synaptic Potential Regularization Has Potential

Enzo Tartaglione*, Daniele Perlo, Marco Grangetto

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


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 ℓ2 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 ℓ2 regularization in deep architectures trained on CIFAR-10.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2019
Subtitle of host publicationDeep Learning - 28th International Conference on Artificial Neural Networks, Proceedings
EditorsIgor V. Tetko, Pavel Karpov, Fabian Theis, Vera Kurková
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783030304836
Publication statusPublished - 2019
Externally publishedYes
Event28th International Conference on Artificial Neural Networks, ICANN 2019 - Munich, Germany
Duration: 17 Sept 201919 Sept 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11728 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference28th International Conference on Artificial Neural Networks, ICANN 2019


  • Classification
  • Generalization
  • Neural networks
  • Post-synaptic potential
  • Regularization


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