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Learning of invariant object recognition in hierarchical neural networks using temporal continuity
Lessmann, Markus

Data: 2015
Resum: There has been a lot of progress in the field of invariant object recognition/categorization in the last decade with several methods trying to mimic functioning of the human visual system (e. g. Neocognitron, HMAX, VisNet). Examining those brain regions is a very difficult task with myriads of details to be considered. To simplify modeling approaches, Jeff Hawkins [1] proposed a framework of three basic principles that might underlie computations in regions of the neocortex. These also form the basis for a capable object recognition system named ”Hierarchical Temporal Memory” (HTM). 1. Learning of temporal sequences for creating invariance to transformations contained in the training data. 2. Learning in a hierarchical structure, in which lower level knowledge can be reused in higher level context and thereby makes memory usage efficient. 3. Prediction of future signals for disambiguation of noisy input by feedback.
Nota: Advisor: Rolf P. Würtz, Institute for Neural Computation, Ruhr-University Bochum, Germany. Date and location of PhD thesis defense: 3 November 2014, Ruhr-University Bochum, Germany
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès.
Document: other ; abstract ; publishedVersion
Matèria: Computer vision ; Object description and recognition ; Machine learning and data mining ; Classification and clustering ; Invariances in recognition
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 14 Núm. 3 (2015) , p.16-18 (Special Issue on Recent PhD Thesis Dissemination (2014)) , ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.719

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