Open AccessPosted Content
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
TL;DR: This work proposes a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN) and successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity.
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Abstract: While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations. To avoid costly pairing, we address the task of discovering cross-domain relations given unpaired data. We propose a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN). Using the discovered relations, our proposed network successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity. Source code for official implementation is publicly available this https URL
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Citations
Point Cloud Completion by Learning Shape Priors
Xiaogang Wang,Marcelo H. Ang,Gim Hee Lee +2 more
- 24 Oct 2020
TL;DR: Zheng et al. as mentioned in this paper proposed a shape prior learning method for point cloud completion, where the shape priors include geometric information in both complete and partial point clouds, and the feature alignment losses consist of a L2 distance and an adversarial loss obtained by maximum mean discrepancy Generative Adversarial Network (MMD-GAN).
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Collaborative Defense-GAN for protecting adversarial attacks on classification system
TL;DR: In this article , the authors proposed a defense framework based on DiscoGANs to discover the relation between attacker and defender characteristics, which can improve the robustness of deep learning models against adversarial attacks.
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GAN-Based Facial Attribute Manipulation
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TL;DR: GAN-based facial attribute manipulation surveys existing methods and explores future directions in the field.
Dose calculation in proton therapy using a discovery cross-domain generative adversarial network (DiscoGAN)
TL;DR: In this paper, a discovery cross-domain generative adversarial network (DiscoGAN) was developed to perform the mapping between two domains: stopping power and dose, with HU values from CT images incorporated as auxiliary features.
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Adversarial Learning for Depth and Viewpoint Estimation From a Single Image
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TL;DR: This work proposes a cross-domain training procedure with 3D CAD models corresponding to objects appearing in real images in order to render depth images from different viewpoints, which outperforms state-of-the-art models on the PASCAL 3D+ dataset.
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Image-to-Image Translation with Conditional Adversarial Networks
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