Conference item
From same photo: Cheating on visual kinship challenges
- Abstract:
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With the propensity for deep learning models to learn unintended signals from data sets there is always the possibility that the network can “cheat” in order to solve a task. In the instance of data sets for visual kinship verification, one such unintended signal could be that the faces are cropped from the same photograph, since faces from the same photograph are more likely to be from the same family. In this paper we investigate the influence of this artefactual data inference in publis...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Accepted manuscript, pdf, 4.4MB)
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- Publisher copy:
- 10.1007/978-3-030-20893-6_41
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Authors
Funding
+ Engineering and Physical Sciences Research Council
More from this funder
Grant:
Systems Biology DTC EP/G03706X/1
Bibliographic Details
- Publisher:
- Springer Publisher's website
- Journal:
- Lecture Notes in Computer Science Journal website
- Volume:
- 11363
- Pages:
- 654-668
- Series:
- Lecture Notes in Computer Science
- Host title:
- Computer Vision – ACCV 2018
- Publication date:
- 2019-05-29
- Acceptance date:
- 2018-09-21
- DOI:
- Source identifiers:
-
938383
- ISBN:
- 9783030208936
Item Description
- Keywords:
- Pubs id:
-
pubs:938383
- UUID:
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uuid:63942c59-387f-4f94-9b19-bf15df8d3609
- Local pid:
- pubs:938383
- Deposit date:
- 2018-11-06
Terms of use
- Copyright holder:
- Springer Nature Switzerland
- Copyright date:
- 2019
- Notes:
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© Springer Nature Switzerland AG 2019. This is the accepted manuscript version of the article. The final version is available online from Springer at: https://doi.org/10.1007/978-3-030-20893-6_41.
This is a conference paper which was presented at Asian Conference on Computer Vision 2018, 4-6 December 2018, Perth, Western Australia
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