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
Unsupervised Reconstruction of Sea Surface Currents from AIS Maritime Traffic Data Using Trainable Variational Models
TL;DR: In this article, the authors investigate the relevance of AIS data streams as a new mean for the estimation of the surface current velocities using a physics-informed observation model, and solve the associated inverse problem using a trainable variational formulation.
Journal ArticleDOI
HeadPose-Softmax: Head pose adaptive curriculum learning loss for deep face recognition
TL;DR: Zhang et al. as mentioned in this paper propose a curriculum learning loss function (HeadPose-Softmax) to classify the difficulty of a sample based on its facial pose, and embed the concept of curriculum learning into the loss function to implement a novel training strategy for deep face recognition.
Journal ArticleDOI
Interactively transforming chinese ink paintings into realistic images using a border enhance generative adversarial network
Chieh-Yu Chung,Szu-Hao Huang +1 more
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Face Frontalization Based on Robustly Fitting a Deformable Shape Model to 3D Landmarks
TL;DR: A robust face alignment method that enables pixel-to-pixel warping and proposes to model inliers and outliers with the generalized Student's t-probability distribution function-a heavy-tailed distribution that is immune to non-Gaussian errors in the data.
Journal ArticleDOI
Decoupled Representation Learning for Character Glyph Synthesis
TL;DR: In this article , a novel model named FontGAN is proposed, which integrates the character structure stylization, de-stylization and texture transfer into a unified framework, and decouple character images into style representation and content representation, which offers fine-grained control of these two types of variables, thus improving the quality of the generated results.
References
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