Dual Encoder-Decoder Based Generative Adversarial Networks for Disentangled Facial Representation Learning

Dual Encoder-Decoder Based Generative Adversarial Networks for Disentangled Facial Representation Learning

To learn disentangled representations of facial images, we present a Dual Encoder-Decoder based Generative Adversarial Network (DED-GAN). In the proposed method, both the generator and discriminator are designed with deep encoder-decoder architectures as their backbones. To be more specific, the enc...

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Bibliographic Details
Main Authors: Cong Hu, Zhenhua Feng, Xiaojun Wu, Josef Kittler
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Disentangled representation learning
encoder-decoder
generative adversarial networks
face synthesis
pose invariant face recognition
Online Access:https://ieeexplore.ieee.org/document/9141259/

Internet

https://ieeexplore.ieee.org/document/9141259/

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