- Abstract:
-
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...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- Files:
-
-
(pdf, 4.4MB)
-
- Publisher copy:
- 10.1007/978-3-030-20893-6_41
-
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- Grant:
- Seebibyte EP/M013774/1
- Systems Biology DTC EP/G03706X/1
- Publisher:
- Springer Publisher's website
- Volume:
- 11363
- Pages:
- 654-668
- Series:
- Lecture Notes in Computer Science
- Publication date:
- 2019-05-29
- Acceptance date:
- 2018-09-21
- DOI:
- Pubs id:
-
pubs:938383
- URN:
-
uri:63942c59-387f-4f94-9b19-bf15df8d3609
- UUID:
-
uuid:63942c59-387f-4f94-9b19-bf15df8d3609
- Local pid:
- pubs:938383
- ISBN:
- 9783030208936
- Copyright holder:
- Springer Nature Switzerland
- Copyright date:
- 2019
- Notes:
-
© 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
Conference item
From same photo: Cheating on visual kinship challenges
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+ Engineering and Physical Sciences Research Council
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