loading
Documents

Research.Publish.Connect.

Paper

Authors: Mina Basirat and Peter Roth

Affiliation: Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria

ISBN: 978-989-758-354-4

Keyword(s): Deep Neural Networks, Activation Functions, Genetic Programming.

Abstract: Deep Neural Networks have been shown to be beneficial for a variety of tasks, in particular allowing for end-to-end learning and reducing the requirement for manual design decisions. However, still many parameters have to be chosen manually in advance, also raising the need to optimize them. One important, but often ignored parameter is the selection of a proper activation function. In this paper, we tackle this problem by learning task-specific activation functions by using ideas from genetic programming. We propose to construct piece-wise activation functions (for the negative and the positive part) and introduce new genetic operators to combine functions in a more efficient way. The experimental results for multi-class classification demonstrate that for different tasks specific activation functions are learned, also outperforming widely used generic baselines.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.172.100.232

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Basirat, M. and Roth, P. (2019). Learning Task-specific Activation Functions using Genetic Programming.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 533-540. DOI: 10.5220/0007408205330540

@conference{visapp19,
author={Mina Basirat. and Peter M. Roth.},
title={Learning Task-specific Activation Functions using Genetic Programming},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={533-540},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007408205330540},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Learning Task-specific Activation Functions using Genetic Programming
SN - 978-989-758-354-4
AU - Basirat, M.
AU - Roth, P.
PY - 2019
SP - 533
EP - 540
DO - 10.5220/0007408205330540

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.