Learning Transformation Invariance from Global to Local

Jens Hocke; Thomas Martinetz

Learning Transformation Invariance from Global to Local

Institut für Neuro- und Bioinformatik

Abstract

Learning representations invariant to image transformations is fundamental to improving object recognition. We explore the connections between i-theory, Toroidal Subspace Analysis and slow subspace learning. All these methods can only achieve invariance to one transformation. Motivated by this limitation of these global methods we adapt the slow subspace approach to a local convolutional setting. Experimentally we show invariance to multiple transformations, and test object recognition performance.

Originalsprache	Englisch
Titel	Workshop New Challenges in Neural Computation 2015
Redakteure/-innen	Barbara Hammer, Thomas Villmann
Seitenumfang	9
Band	03
Erscheinungsdatum	2015
Seiten	16-24
Publikationsstatus	Veröffentlicht - 2015
Veranstaltung	37th German Conference on Pattern Recognition - Workshop Program: New Challenges in Neural Computation (NC2) - RWTH Aachen, Aachen, Deutschland Dauer: 10.10.2015 → 10.10.2015 http://vmv2015.rwth-aachen.de/workshops.html

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

SDG 3 – Gesundheit und Wohlergehen
SDG 9 – Industrie, Innovation und Infrastruktur

Dokumente

http://www.inb.uni-luebeck.de/publications/pdfs/HoMa15.pdf

Zitieren

@inproceedings{ad5a63f405e24c3abed530c81f12429b,

title = "Learning Transformation Invariance from Global to Local",

abstract = "Learning representations invariant to image transformations is fundamental to improving object recognition. We explore the connections between i-theory, Toroidal Subspace Analysis and slow subspace learning. All these methods can only achieve invariance to one transformation. Motivated by this limitation of these global methods we adapt the slow subspace approach to a local convolutional setting. Experimentally we show invariance to multiple transformations, and test object recognition performance.",

author = "Jens Hocke and Thomas Martinetz",

year = "2015",

language = "English",

volume = "03",

series = "Machine Learning Reports",

pages = "16--24",

editor = "Barbara Hammer and Thomas Villmann",

booktitle = "Workshop New Challenges in Neural Computation 2015",

note = "37th German Conference on Pattern Recognition - Workshop Program ; Conference date: 10-10-2015 Through 10-10-2015",

url = "http://vmv2015.rwth-aachen.de/workshops.html",

}

Hocke, J & Martinetz, T 2015, Learning Transformation Invariance from Global to Local. in B Hammer & T Villmann (Hrsg.), Workshop New Challenges in Neural Computation 2015. Bd. 03, Machine Learning Reports, S. 16-24, 37th German Conference on Pattern Recognition - Workshop Program, Aachen, Deutschland, 10.10.15. <http://www.inb.uni-luebeck.de/publications/pdfs/HoMa15.pdf>

TY - GEN

T1 - Learning Transformation Invariance from Global to Local

AU - Hocke, Jens

AU - Martinetz, Thomas

PY - 2015

Y1 - 2015

N2 - Learning representations invariant to image transformations is fundamental to improving object recognition. We explore the connections between i-theory, Toroidal Subspace Analysis and slow subspace learning. All these methods can only achieve invariance to one transformation. Motivated by this limitation of these global methods we adapt the slow subspace approach to a local convolutional setting. Experimentally we show invariance to multiple transformations, and test object recognition performance.

AB - Learning representations invariant to image transformations is fundamental to improving object recognition. We explore the connections between i-theory, Toroidal Subspace Analysis and slow subspace learning. All these methods can only achieve invariance to one transformation. Motivated by this limitation of these global methods we adapt the slow subspace approach to a local convolutional setting. Experimentally we show invariance to multiple transformations, and test object recognition performance.

M3 - Conference contribution

VL - 03

T3 - Machine Learning Reports

SP - 16

EP - 24

BT - Workshop New Challenges in Neural Computation 2015

A2 - Hammer, Barbara

A2 - Villmann, Thomas

T2 - 37th German Conference on Pattern Recognition - Workshop Program

Y2 - 10 October 2015 through 10 October 2015

ER -