Deep Learning Approach To Face Conditioning Using Invertible Conditional Generative Adversarial Networks (ICGAN)

Authors

  • Utpal Shrivastava Department of Computer Science, Amity University, Gurgaon, Haryana, India Author
  • Vikas Thada Department of Computer Science, Amity University, Gurgaon, Haryana, India Author
  • Amit Kumar Department of Computer Science, Amity University, Gurgaon, Haryana, India Author
  • Maulik Garach Department of Computer Science, Amity University, Gurgaon, Haryana, India Author
  • Adit Paliwal Department of Computer Science, Amity University, Gurgaon, Haryana, India Author

Keywords:

ICGAN, Face Conditioning, Deep Learning

Abstract

 We propose another system for evaluating  generative models by means of an ill-disposed process, in  which we at the same time train two models: a generative  model G that catches the information conveyance, and a  discriminative model D that gauges the likelihood that an  example originated from the preparation information as  opposed to G. The preparation strategy for G is to expand  the likelihood of D committing an error. This system  compares to a minimax two-player game. In the space of  discretionary capacities G and D, an interesting  arrangement exists, with G recuperating the preparation  information conveyance and D equivalent to 1/2 all over the  place. For the situation where G and D are characterized by  multilayer perceptions, the whole framework can be  prepared with back propagation. There is no requirement  for any Markov chains or unrolled estimated deduction  systems during either preparing or age of tests.  Investigations illustrate the capability of the system through  subjective and quantitative assessment of the produced  tests. 

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References

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Published

2020-05-05

Issue

Vol. 8 No. 3 (2020): International Journal of Innovative Research in Computer Science and Technology (May) 2020

Section

Articles

How to Cite

Deep Learning Approach To Face Conditioning Using Invertible Conditional Generative Adversarial Networks (ICGAN) . (2020). International Journal of Innovative Research in Computer Science & Technology, 8(3), 164–170. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/13289