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dc.contributor.authorDoaud, Maisaen_NZ
dc.contributor.authorMayo, Michaelen_NZ
dc.contributor.editorTorra, V.en_NZ
dc.contributor.editorNarukawa, Y.en_NZ
dc.contributor.editorHonda, A.en_NZ
dc.contributor.editorInoue, S.en_NZ
dc.coverage.spatialKitakyushu, Japanen_NZ
dc.date.accessioned2019-02-18T22:40:58Z
dc.date.available2017en_NZ
dc.date.available2019-02-18T22:40:58Z
dc.date.issued2017en_NZ
dc.identifier.citationDoaud, M., & Mayo, M. (2017). Using swarm optimization to enhance autoencoder’s images. In V. Torra, Y. Narukawa, A. Honda, & S. Inoue (Eds.), USB Proceedings of 14th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2017) (pp. 118–131). Kitakyushu, Japan.en
dc.identifier.isbn978-84-697-6794-8en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12344
dc.description.abstractAutoencoders learn data representations through reconstruction. Robust training is the key factor affecting the quality of the learned representations and, consequently, the accuracy of the application that use them. Previous works suggested methods for deciding the optimal autoencoder configuration which allows for robust training. Nevertheless, improving the accuracy of a trained autoencoder has got limited, if no, attention. We propose a new approach that improves the accuracy of a trained autoencoder’s results and answers the following question, Given a trained autoencoder, a test image, and using a real-parameter optimizer, can we generate better quality reconstructed image version than the one generated by the autoencoder?. Our proposed approach combines both the decoder part of a trained Resitricted Boltman Machine-based autoencoder with the Competitive Swarm Optimization algorithm. Experiments show that it is possible to reconstruct images using trained decoder from randomly initialized representations. Results also show that our approach reconstructed better quality images than the autoencoder in most of the test cases. Indicating that, we can use the approach for improving the performance of a pre-trained autoencoder if it does not give satisfactory results.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.sourceMDAI 2017en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectrepresentationsen_NZ
dc.subjectoptimizationen_NZ
dc.subjectautoencodersen_NZ
dc.subjectreconstructionen_NZ
dc.subjectMachine learning
dc.titleUsing swarm optimization to enhance autoencoder’s imagesen_NZ
dc.typeConference Contribution
dc.relation.isPartOfUSB Proceedings of 14th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2017)en_NZ
pubs.begin-page118
pubs.elements-id211390
pubs.end-page131
pubs.finish-date2017-10-20en_NZ
pubs.start-date2017-10-18en_NZ


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