Proceedings ArticleDOI
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition
Luan Tran,Xi Yin,Xiaoming Liu +2 more
- pp 1283-1292
TLDR
Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.Abstract:
The large pose discrepancy between two face images is one of the key challenges in face recognition. Conventional approaches for pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks jointly to allow them to leverage each other. To this end, this paper proposes Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with three distinct novelties. First, the encoder-decoder structure of the generator allows DR-GAN to learn a generative and discriminative representation, in addition to image synthesis. Second, this representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator. Third, DR-GAN can take one or multiple images as the input, and generate one unified representation along with an arbitrary number of synthetic images. Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.read more
Citations
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Occlusion-Invariant Rotation-Equivariant Semi-Supervised Depth Based Cross-View Gait Pose Estimation.
Xiao Gu,Jianxin Yang,Hanxiao Zhang,Jianing Qiu,Frank P.-W. Lo,Yao Guo,Guang-Zhong Yang,Benny Lo +7 more
TL;DR: In this article, a semi-supervised learning framework is proposed for cross-view generalization with an occlusion-invariant semi supervised learning framework built upon a novel rotation-equivariant backbone, which generalizes well on the real-world data from all the other unseen views.
Journal ArticleDOI
Rotation and Translation Invariant Representation Learning with Implicit Neural Representations
TL;DR: In this paper , an implicit neural representation (INRNN) with a hypernetwork is used to obtain semantic representations disentangled from the orientation of the image, which can effectively learn disentanglement semantic representations on more complex images.
Journal ArticleDOI
A review of disentangled representation learning for visual data processing and analysis
TL;DR: In this paper , the authors proposed a method to improve the quality of the training environment for teachers in the field of education by using the knowledge of the students' own knowledge of their teachers.
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