Detailed Information
Cited time in 
Cited time in 
Cited time in
Metadata Downloads
Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Kim, Sungil | - |
| dc.contributor.author | Yoo, Ji Tae | - |
| dc.date.accessioned | 2024-01-29T16:32:57Z | - |
| dc.date.available | 2024-01-29T16:32:57Z | - |
| dc.date.issued | 2023-08 | - |
| dc.description.degree | Master | - |
| dc.description | Graduate School of Artificial Intelligence | - |
| dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/74263 | - |
| dc.identifier.uri | http://unist.dcollection.net/common/orgView/200000694569 | - |
| dc.language | eng | - |
| dc.publisher | Ulsan National Institute of Science and Technology (UNIST) | - |
| dc.rights.embargoReleaseDate | 9999-12-31 | - |
| dc.rights.embargoReleaseTerms | 9999-12-31 | - |
| dc.subject | Anomaly detection. Contaminated dataset, Semi-supervised learning, Line quality control systems | - |
| dc.title | Enhanced Deep Anomaly Detection In Contaminated Datasets Using Semi-Supervised Learning | - |
| dc.type | Thesis | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.