Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Convolutional Extreme Learning Machines: A Systematic Review

Version 1 : Received: 27 April 2021 / Approved: 28 April 2021 / Online: 28 April 2021 (15:31:14 CEST)

A peer-reviewed article of this Preprint also exists.

Rodrigues, I.R.; da Silva Neto, S.R.; Kelner, J.; Sadok, D.; Endo, P.T. Convolutional Extreme Learning Machines: A Systematic Review. Informatics 2021, 8, 33. Rodrigues, I.R.; da Silva Neto, S.R.; Kelner, J.; Sadok, D.; Endo, P.T. Convolutional Extreme Learning Machines: A Systematic Review. Informatics 2021, 8, 33.

Journal reference: Informatics 2021, 8, 33
DOI: 10.3390/informatics8020033

Abstract

Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

Subject Areas

Convolutional extreme learning machine; Deep learning; Multimedia analysis

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