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
Visually Interpretable Representation Learning for Depression Recognition from Facial Images
TL;DR: A deep regression network termed DepressNet is presented to learn a depression representation with visual explanation, with results showing that the DAM induced by the learned deep model may help reveal the visual depression pattern on faces and understand the insights of automated depression diagnosis.
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Multi-Content GAN for Few-Shot Font Style Transfer
TL;DR: This article proposed an end-to-end stacked conditional GAN model considering content along channels and style along network layers to generate a set of multi-content images following a consistent style from very few examples.
Journal ArticleDOI
A survey of image synthesis and editing with generative adversarial networks
Xian Wu,Kun Xu,Peter Hall +2 more
TL;DR: This paper surveys recent GAN papers regarding topics including, but not limited to, texture synthesis, image inpainting, image-to-image translation, and image editing.
Proceedings ArticleDOI
Towards High-Fidelity Nonlinear 3D Face Morphable Model
Luan Tran,Feng Liu,Xiaoming Liu +2 more
TL;DR: In this article, a dual-pathway network is proposed to learn additional proxies as means to side-step strong regularizations, as well as, leverages to promote detailed shape/albedo.
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Transfer Adaptation Learning: A Decade Survey
B Rajesh Kumar,Lei Zhang +1 more
TL;DR: This paper surveys the recent advances in transfer adaptation learning methodology and potential benchmarks and provides researchers a framework for better understanding and identifying the research status, challenges and future directions of the field.
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