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The Conditional Boundary Equilibrium Generative Adversarial Network and its Application to Facial Attributes
Publikationstyp
Conference Paper
Date Issued
2019-07
Sprache
English
Volume
2019-July
Article Number
8852164
Citation
International Joint Conference on Neural Networks (IJCNN 2019)
Contribution to Conference
Publisher DOI
Scopus ID
We propose an extension of the Boundary Equilibrium GAN (BEGAN) neural network, named Conditional BEGAN (CBEGAN), as a general generative and transformational approach for data processing. As a novelty, the system is able of both data generation and transformation under conditional input. We evaluate our approach for conditional image generation and editing using five controllable attributes for images of faces from the CelebA dataset: age, smiling, cheekbones, eyeglasses and gender. We perform a set of objective quantitative experiments to evaluate the model's performance and a qualitative user study to evaluate how humans assess the generated and edited images. Both evaluations yield coinciding results which show that the generated facial attributes are recognizable in more than 80% of all new testing samples.
Subjects
Conditional GAN
image generation
image translation