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dc.contributor.authorVetrova, Varvaraen_NZ
dc.contributor.authorCoup, Sheldonen_NZ
dc.contributor.authorFrank, Eibeen_NZ
dc.contributor.authorCree, Michael J.en_NZ
dc.coverage.spatialConference held at Salt Lake City, UTen_NZ
dc.date.accessioned2018-08-01T22:18:50Z
dc.date.available2018en_NZ
dc.date.available2018-08-01T22:18:50Z
dc.date.issued2018en_NZ
dc.identifier.citationVetrova, V., Coup, S., Frank, E., & Cree, M. J. (2018). Difference in details: transfer learning case study of ‘cryptic’ plants and moths. Presented at the The Fifth Workshop on Fine-Grained Visual Categorization held in conjunction with CVPR 2018, Conference Website.en
dc.identifier.urihttps://hdl.handle.net/10289/12006
dc.description.abstractCan we classify species of very similar looking organisms quickly and accurately using only out of the box feature transfer? What if we only have small number of images? This experimental paper is part of on-going project on species recognition research and evaluates transfer learning and fine-tuning approaches on two highly specialized fine-grained datasets. The two fine-grained datasets were specifically assembled for the purpose of this research. These datasets consist of images of New Zealand native species of moths and ”cryptic” plants of Genus Corposma found also in New Zealand. We compare results from finetuning experiments with performance of transfer learning without fine-tuning. The latter results are based on features extracted from various levels of depth in the InceptionV3 network, including fully connected layers. The extracted features serve as inputs to a number of classification algorithms. We observe contrasting results for the two datasets. For the dataset of moths, the method based on features extracted from deep levels of the InceptionV3 network outperforms fine-tuning in accuracy (90.09% versus 87.18%). This is not the case for the dataset of cryptic plants (60.46% versus 74.37%). Despite both datasets being fine-grained in nature, these experimental differences could be attributed to intrinsically different morphology of organisms and warrant further investigation.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.urihttps://sites.google.com/view/fgvc5/program
dc.rights© 2018 copyright with the authors.
dc.sourceThe Fifth Workshop on Fine-Grained Visual Categorization held in conjunction with CVPR 2018en_NZ
dc.subjectMachine learning
dc.titleDifference in details: transfer learning case study of "cryptic" plants and mothsen_NZ
dc.typeConference Contribution
pubs.elements-id225657
pubs.finish-date2018-06-22en_NZ
pubs.place-of-publicationConference Websiteen_NZ
pubs.publisher-urlhttps://sites.google.com/view/fgvc5/programen_NZ
pubs.start-date2018-06-22en_NZ


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