UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Avoiding degradation in deep feed-forward networks by phasing out skip-connections

Monti, RP; Tootoonian, S; Cao, R; (2018) Avoiding degradation in deep feed-forward networks by phasing out skip-connections. In: Proceedings of the International Conference on Artificial Neural Networks and Machine Learning : ICANN 2018. (pp. pp. 447-456). Springer: Rhodes, Greece. Green open access

[thumbnail of samplepaper.pdf]
Preview
Text
samplepaper.pdf - Accepted Version

Download (1MB) | Preview

Abstract

A widely observed phenomenon in deep learning is the degradation problem: increasing the depth of a network leads to a decrease in performance on both test and training data. Novel architectures such as ResNets and Highway networks have addressed this issue by introducing various flavors of skip-connections or gating mechanisms. However, the degradation problem persists in the context of plain feed-forward networks. In this work we propose a simple method to address this issue. The proposed method poses the learning of weights in deep networks as a constrained optimization problem where the presence of skip-connections is penalized by Lagrange multipliers. This allows for skip-connections to be introduced during the early stages of training and subsequently phased out in a principled manner. We demonstrate the benefits of such an approach with experiments on MNIST, fashion-MNIST, CIFAR-10 and CIFAR-100 where the proposed method is shown to greatly decrease the degradation effect and is often competitive with ResNets.

Type: Proceedings paper
Title: Avoiding degradation in deep feed-forward networks by phasing out skip-connections
Event: International Conference on Artificial Neural Networks and Machine Learning : ICANN 2018
ISBN-13: 9783030014230
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-01424-7_44
Publisher version: https://doi.org/10.1007/978-3-030-01424-7_44
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Degradation, Shattered/vanishing gradients, Skip-connections
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/10061069
Downloads since deposit
737Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item