Future frame prediction based on generative assistant discriminative network for anomaly detection.
Saved in:
| Title: | Future frame prediction based on generative assistant discriminative network for anomaly detection. |
|---|---|
| Authors: | Li, Chaobo1, Li, Hongjun1,2, lihongjun@ntu.edu.cn, Zhang, Guoan1 |
| Source: | Applied Intelligence; Jan2023, Vol. 53 Issue 1, p542-559, 18p |
| Database: | Applied Science & Technology Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 161102604 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Future frame prediction based on generative assistant discriminative network for anomaly detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Li%2C+Chaobo%22">Li, Chaobo</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AU" term="%22Li%2C+Hongjun%22">Li, Hongjun</searchLink><relatesTo>1,2</relatesTo>, <i>lihongjun@ntu.edu.cn</i><br /><searchLink fieldCode="AU" term="%22Zhang%2C+Guoan%22">Zhang, Guoan</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Intelligence%22">Applied Intelligence</searchLink>; Jan2023, Vol. 53 Issue 1, p542-559, 18p |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=161102604 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10489-022-03488-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 542 Titles: – TitleFull: Future frame prediction based on generative assistant discriminative network for anomaly detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Chaobo – PersonEntity: Name: NameFull: Li, Hongjun – PersonEntity: Name: NameFull: Zhang, Guoan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 0924669X Numbering: – Type: volume Value: 53 – Type: issue Value: 1 Titles: – TitleFull: Applied Intelligence Type: main |
| ResultId | 1 |