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
Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution
TL;DR: The Semi-Cycled Generative Adversarial Networks (SCGAN) is able to alleviate the adverse effects of the domain gap between the real-world LR face images and the synthetic LR ones, and to achieve accurate and robust face SR performance by the shared restoration branch regularized by both the forward and backward cycle-consistent learning processes.
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Unpaired Speech Enhancement by Acoustic and Adversarial Supervision for Speech Recognition
TL;DR: In this article, an alternative learning algorithm, called acoustic and adversarial supervision (AAS), is proposed to make the enhanced output both maximizing the likelihood of transcription on the pre-trained acoustic model and having general characteristics of clean speech, which improve generalization on unseen noisy speeches.
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Informative Multimodal Unsupervised Image-to-Image Translation
Tien Tai Doan,Guillaume Ghyselinck,Blaise Hanczar +2 more
- 24 May 2021
TL;DR: This work proposes a new method of multimodal image translation, called InfoMUNIT, which is an extension of the state-of-the-art method MUNIT that allows controlling the style of the generated images and improves their quality and diversity.
Prediction of face age progression with generative adversarial networks
TL;DR: Zhang et al. as discussed by the authors proposed a realistic face aging method using AttentionGAN and SRGAN, which uses two separate subnets in a generator to generate multiple attention masks and multiple content masks, and then multiplied with the corresponding content mask along with an input image to finally achieve the desired results.
Lumbar Spine Computed Tomography to Magnetic Resonance Imaging Synthesis Using Generative Adversarial Network: Visual Turing Test
Ki-Taek Hong,Yongwon Cho,Chang Ho Kang,Kyung Sik Ahn,H. Lee,Joohee Kim,Suk Joo Hong,Baek Hyun Kim,Euddeum Shim +8 more
TL;DR: GAN was applied to synthesize lumbar spine MR images from CT images and compare training algorithms of the GAN were fairly realistic and the supervised training algorithm was found to provide the closest image to true images.
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Generative Adversarial Nets
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•Posted Content
Image-to-Image Translation with Conditional Adversarial Networks
TL;DR: Conditional Adversarial Network (CA) as discussed by the authors is a general-purpose solution to image-to-image translation problems, which can be used to synthesize photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
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