dc.citation.conferencePlace |
NE |
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dc.citation.conferencePlace |
Amsterdam |
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dc.citation.endPage |
101 |
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dc.citation.startPage |
88 |
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dc.citation.title |
14th European Conference on Computer Vision, ECCV 2016 |
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dc.contributor.author |
Goo, Wonjoon |
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dc.contributor.author |
Kim, Juyong |
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dc.contributor.author |
Kim, Gunhee |
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dc.contributor.author |
Hwang, Sung Ju |
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dc.date.accessioned |
2023-12-19T20:07:27Z |
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dc.date.available |
2023-12-19T20:07:27Z |
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dc.date.created |
2016-12-26 |
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dc.date.issued |
2016-10-11 |
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dc.description.abstract |
We propose a novel convolutional network architecture that abstracts and dierentiates the categories based on a given class hier- archy. We exploit grouped and discriminative information provided by the taxonomy, by focusing on the general and specic components that comprise each category, through the min- and dierence-pooling operations. Without using any additional parameters or substantial increase in time complexity, our model is able to learn the features that are discriminative for classifying often confused sub-classes belonging to the same superclass, and thus improve the overall classication performance. We validate our method on CIFAR-100, Places-205, and ImageNet Animal datasets, on which our model obtains signicant improvements over the base convolutional networks. |
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dc.identifier.bibliographicCitation |
14th European Conference on Computer Vision, ECCV 2016, pp.88 - 101 |
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dc.identifier.doi |
10.1007/978-3-319-46475-6_6 |
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dc.identifier.issn |
0302-9743 |
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dc.identifier.scopusid |
2-s2.0-84990845581 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/35371 |
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dc.identifier.url |
https://link.springer.com/chapter/10.1007%2F978-3-319-46475-6_6 |
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dc.language |
영어 |
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dc.publisher |
14th European Conference on Computer Vision, ECCV 2016 |
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dc.title |
Taxonomy-Regularized Semantic Deep Convolutional Neural Networks |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2016-10-08 |
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