Evolving Deep DenseBlock Architecture Ensembles for Image Classification

Fielding, Ben and Zhang, Li (2020) Evolving Deep DenseBlock Architecture Ensembles for Image Classification. Electronics, 9 (11). p. 1880. ISSN 2079-9292

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Official URL: https://doi.org/10.3390/electronics9111880


Automatic deep architecture generation is a challenging task, owing to the large number of controlling parameters inherent in the construction of deep networks. The combination of these parameters leads to the creation of large, complex search spaces that are feasibly impossible to properly navigate without a huge amount of resources for parallelisation. To deal with such challenges, in this research we propose a Swarm Optimised DenseBlock Architecture Ensemble (SODBAE) method, a joint optimisation and training process that explores a constrained search space over a skeleton DenseBlock Convolutional Neural Network (CNN) architecture. Specifically, we employ novel weight inheritance learning mechanisms, a DenseBlock skeleton architecture, as well as adaptive Particle Swarm Optimisation (PSO) with cosine search coefficients to devise networks whilst maintaining practical computational costs. Moreover, the architecture design takes advantage of recent advancements of the concepts of residual connections and dense connectivity, in order to yield CNN models with a much wider variety of structural variations. The proposed weight inheritance learning schemes perform joint optimisation and training of the architectures to reduce the computational costs. Being evaluated using the CIFAR-10 dataset, the proposed model shows great superiority in classification performance over other state-of-the-art methods while illustrating a greater versatility in architecture generation.

Item Type: Article
Additional Information: Funding information: This work was supported in part by RPPTV Ltd., West Sussex, UK and in part by Northumbria University for jointly funding an industrial collaborative Ph.D. studentship.
Uncontrolled Keywords: machine learning; evolutionary computation; optimisation; computer vision
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 09 Nov 2020 13:16
Last Modified: 27 Aug 2021 13:44
URI: http://nrl.northumbria.ac.uk/id/eprint/44712

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