Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 정희철 | - |
dc.contributor.author | 김준광 | - |
dc.contributor.author | 이상철 | - |
dc.contributor.author | 최민국 | - |
dc.contributor.author | 정우영 | - |
dc.date.accessioned | 2023-12-26T20:43:45Z | - |
dc.date.available | 2023-12-26T20:43:45Z | - |
dc.date.created | 2017-11-05 | - |
dc.date.issued | 2017-11-11 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/47072 | - |
dc.description.abstract | When we perform a recognition task using deep neural networks, the recognition rate drops because of the difference between the distribution of training data and test data. To solve this problem, we propose a domain adaptation method using a style transfer method. In our method, the style transfer is performed to the target data so that it looks like the source domain. The transferred images are used for the input to the deep neural network trained by the image of the source domain. Finally, we show that our proposed method is effective to improve the recognition rate. | - |
dc.publisher | 대한임베디드공학회 | - |
dc.title | Style Transfer 기반 Unsupervised Domain Adaptation | - |
dc.type | Conference Paper | - |
dc.identifier.bibliographicCitation | 2017 대한임베디드공학회 추계학술대회 | - |
dc.citation.conferencePlace | KO | - |
dc.citation.conferencePlace | 제주도 | - |
dc.citation.title | 2017 대한임베디드공학회 추계학술대회 | - |
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