Active object recognition using hierarchical local-receptive-field-based extreme learning machine

Much work has recently identified the need to combine deep learning with extreme learning in order to strike a performance balance with accuracy, especially in the domain of multimedia applications. When considering this new paradigm—namely, the convolutional extreme learning machine (CELM)—we present a systematic review that investigates alternative deep learning architectures that use the extreme learning machine (ELM) for faster training to solve problems that are based on image analysis. We detail each of the architectures that are found in the literature along with their application scenarios, benchmark datasets, main results, and advantages, and then present the open challenges for CELM. We followed a well-structured methodology and established relevant research questions that guided our findings. Based on 81 primary studies, we found that object recognition is the most common problem that is solved by CELM, and CCN with predefined kernels is the most common CELM architecture proposed in the literature. The results from experiments show that CELM models present good precision, convergence, and computational performance, and they are able to decrease the total processing time that is required by the learning process. The results presented in this systematic review are expected to contribute to the research area of CELM, providing a good starting point for dealing with some of the current problems in the analysis of computer vision based on images.

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“…The multilayer ELM-LRF is another known ELM-LRF variation that consists of multiple convolution and pooling layers [27,38,51,67,77,114].…”

Section: Cnn With Predefined Kernels For Feature Extraction and Elm For Fast Learningmentioning

confidence: 99%

Convolutional Extreme Learning Machines: A Systematic Review

et al. 2021

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“…The multilayer ELM-LRF is another known ELM-LRF variation which consists of multiple convolution and pooling layers [67], [27], [38], [51], [114], and [77].…”

Section: Random Filtermentioning

confidence: 99%

Convolutional Extreme Learning Machines: A Systematic Review

Rodrigues¹,

Rogério²,

Kelner³

et al. 2021

Preprint

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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.

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“…In [98], a hierarchical local-receptive-field-based ELM structure was proposed to jointly learn the state representation and the reinforcement learning strategy. As shown in Fig.…”

Section: Input Map Feature Map Pooling Mapmentioning

confidence: 99%

Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines

Zhang

Xiao

et al. 2020

Journal of the Franklin Institute

158

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In the past decade, deep learning techniques have powered many aspects of our daily life, and drawn ever-increasing research interests. However, conventional deep learning approaches, such as deep belief network (DBN), restricted Boltzmann machine (RBM), and convolutional neural network (CNN), suffer from time-consuming training process due to fine-tuning of a large number of parameters and the complicated hierarchical structure. Furthermore, the above complication makes it difficult to theoretically analyze and prove the universal approximation of those conventional deep learning approaches. In order to tackle the issues, multilayer extreme learning machines (ML-ELM) were proposed, which accelerate the development of deep learning. Compared with conventional deep learning, ML-ELMs are non-iterative and fast due to the random feature mapping mechanism. In this paper, we perform a thorough review on the development of ML-ELMs, including stacked ELM autoencoder (ELM-AE), residual ELM, and local receptive field based ELM (ELM-LRF), as well as address their applications. In addition, we also discuss the connection between random neural networks and conventional deep learning.

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Active object recognition using hierarchical local-receptive-field-based extreme learning machine

Cited by 22 publications

References 22 publications

Convolutional Extreme Learning Machines: A Systematic Review

Convolutional Extreme Learning Machines: A Systematic Review

Convolutional Extreme Learning Machines: A Systematic Review

Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines

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