articleJul 1, 2017Closed access

Age Progression/Regression by Conditional Adversarial Autoencoder

University of Tennessee at Knoxville

Indexed incrossref

Abstract

If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5? The answer is probably a No. Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with…

Citation impact

1,087
total citations
FWCI
28.00
Percentile
100%
References
41
Citations per year

Authors

3

Topics & keywords

Keywords
  • Autoencoder
  • Artificial intelligence
  • Generator (circuit theory)
  • Face (sociological concept)
  • Computer science
  • Pattern recognition (psychology)
  • Image (mathematics)
  • Encoder
UN Sustainable Development Goals
  • Reduced inequalities
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