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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Proceedings ArticleDOI
Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network
TL;DR: An extensive set of qualitative and quantitative experiments show that the appearance and geometric information can be well disentangled, and the learned geometric generator can be conveniently transferred to the other image datasets to facilitate knowledge transfer tasks.
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Feature Transfer Learning for Deep Face Recognition with Long-Tail Data.
TL;DR: This paper proposes to handle long-tail classes in the training of a face recognition engine by augmenting their feature space under a center-based feature transfer framework, which allows smooth visual interpolation, which demonstrates disentanglement to preserve identity of a class while augmenting its feature space with non-identity variations.
Journal ArticleDOI
Deep face recognition with clustering based domain adaptation
Mei Wang,Weihong Deng +1 more
TL;DR: A new clustering-based domain adaptation method designed for face recognition task in which the source and target domain do not share any classes, which effectively learns discriminative target representation.
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Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
TL;DR: A novel Deep Residual EquivAriant Mapping (DREAM) block is formulated, which is capable of adaptively adding residuals to the input deep representation to transform a profile face representation to a canonical pose that simplifies recognition.
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Guarding Against Adversarial Domain Shifts with Counterfactual Regularization.
TL;DR: A causal framework for the problem is provided and groups of instances of the same object are treated as counterfactuals under different interventions on the mutable style features and links to questions of fairness, transfer learning and adversarial examples are shown.
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