English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

To learn disentangled representations of facial images, we present a DualEncoder-Decoder based Generative Adversarial Network (DED-GAN). In the proposedmethod, both the generator and discriminator are designed with deepencoder-decoder architectures as their backbones. To be more specific, theencoder-decoder structured generator is used to learn a pose disentangled facerepresentation, and the encoder-decoder structured discriminator is tasked toperform real/fake classification, face reconstruction, determining identity andestimating face pose. We further improve the proposed network architecture byminimising the additional pixel-wise loss defined by the Wasserstein distanceat the output of the discriminator so that the adversarial framework can bebetter trained. Additionally, we consider face pose variation to be continuous,rather than discrete in existing literature, to inject richer pose informationinto our model. The pose estimation task is formulated as a regression problem,which helps to disentangle identity information from pose variations. Theproposed network is evaluated on the tasks of pose-invariant face recognition(PIFR) and face synthesis across poses. An extensive quantitative andqualitative evaluation carried out on several controlled and in-the-wildbenchmarking datasets demonstrates the superiority of the proposed DED-GANmethod over the state-of-the-art approaches.

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