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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Journal ArticleDOI
One-class anomaly detection via novelty normalization
TL;DR: In this paper, an autoencoder network with a normalization term was proposed for one-class anomaly detection, where after training on a singular class, they try to determine whether or not inputs belong to that said class.
Dissertation
Apprentissage neuronal profond pour l'analyse de contenus multimodaux et temporels
TL;DR: Une representation neuronale plus generale est obtenue a partir d’un modele unique, qui rassemble the connaissance contenue dans les modeles pre-entraines et conduit a des performances a l'etat de l'art sur une variete of tâches d'analyse de visages.
Journal ArticleDOI
Realistic frontal face reconstruction using coupled complementarity of far-near-sighted face images
TL;DR: Wang et al. as discussed by the authors proposed a dual-branch HR frontal face reconstruction network to explicitly exploit coupled complementarity hidden in the far-near face images of the same subject, where one branch performs super-resolution (SR) of the LR frontal face and the other branch performs detail fusion and holistic compensation between multiple HR tilted faces as well as the super-resolved frontal result.
Journal ArticleDOI
Domain-Adversarial-Guided Siamese Network for Unsupervised Cross-Domain 3-D Object Retrieval
TL;DR: Wang et al. as mentioned in this paper proposed a domain-adversarial guided siamese network (DAGSN) for unsupervised cross-domain 3-D object retrieval (CD3DOR).
Journal ArticleDOI
Model Assumptions and Data Characteristics: Impacts on Domain Adaptation in Building Segmentation
TL;DR: A large-scale study across over 200 DA scenarios that include variations across view angles, areas observed, and sensors used for data acquisition, and a detailed meta-analysis of experiments highlighting the importance of accurately considering data assumptions for DA in RS segmentation tasks is provided.
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Posted Content
Conditional Generative Adversarial Nets
Mehdi Mirza,Simon Osindero +1 more
TL;DR: The conditional version of generative adversarial nets is introduced, which can be constructed by simply feeding the data, y, to the generator and discriminator, and it is shown that this model can generate MNIST digits conditioned on class labels.