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
Clusterface: Joint Clustering and Classification for Set-Based Face Recognition
TL;DR: In this article, a joint clustering and classification scheme is proposed to learn deep face associations in an easy-to-hard way, where the early iterations tend to preserve high reliability.
Journal ArticleDOI
Domain-Adversarial-Guided Siamese Network for Unsupervised Cross-Domain 3-D Object Retrieval
TL;DR: This article proposes a domain-adversarial guided siamese network (DAGSN) for unsupervised cross-domain 3-D object retrieval (CD3DOR) and demonstrates that the proposed DAGSN can significantly outperform state-of-the-art CD3D OR methods.
Proceedings ArticleDOI
Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks
TL;DR: Wang et al. as discussed by the authors proposed a unified framework for vertical federated knowledge transfer mechanism (VFedTrans) based on a novel cross-hospital representation distillation component to improve the information sharing capability and innovation of various healthcare-related institutions.
Posted Content
Bi-Directional Domain Translation for Zero-Shot Sketch-Based Image Retrieval
TL;DR: A Bi-directional Domain Translation (BDT) framework is proposed for ZS-SBIR, in which the image domain and sketch domain can be translated to each other through disentangled structure and appearance features to facilitate structure-based retrieval.
Book ChapterDOI
Probabilistic Estimation of Evaporated Water in Cooling Towers Using a Generative Adversarial Network
TL;DR: A generative model is proposed which is able to generalize the estimation of the evaporated water, even in situations not included in the training data, which is tested using real data from a cooling tower located at the Hospital of Leon.
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