Unpaired photo-to-caricature translation on faces in the wild
TLDR
Zheng et al. as mentioned in this paper designed a dual pathway model with one coarse discriminator and one fine discriminator to capture global structure with local statistics while translation, which can also be used for other high-level image-to-image translation tasks.About:
This article is published in Neurocomputing.The article was published on 2019-08-25 and is currently open access. It has received 24 citations till now.read more
Citations
More filters
Journal ArticleDOI
TumorGAN: A Multi-Modal Data Augmentation Framework for Brain Tumor Segmentation.
TL;DR: The experimental results demonstrate that the synthetic data pairs generated by the proposed TumorGAN method can practically improve tumor segmentation performance when applied to segmentation network training.
Journal ArticleDOI
Image-to-Image Translation: Methods and Applications
TL;DR: Image-to-image translation (I2I) aims to transfer images from a source domain to a target domain while preserving the content representations as mentioned in this paper , which has drawn increasing attention and made tremendous progress in recent years.
Journal ArticleDOI
CSGAN: Cyclic-Synthesized Generative Adversarial Networks for image-to-image transformation
TL;DR: The proposed CSGAN uses a new objective function (loss) called Cyclic-Synthesized Loss (CS) between the synthesized image of one domain and cycled image of another domain and exhibits the promising and comparable performance over Facades dataset in terms of both qualitative and quantitative measures.
Journal ArticleDOI
PCSGAN: Perceptual cyclic-synthesized generative adversarial networks for thermal and NIR to visible image transformation
TL;DR: Kishan Kancharagunta et al. as mentioned in this paper proposed a perceptual cyclic-synthesized generative adversarial network (PCSGAN) for image transformation.
Journal ArticleDOI
Unsupervised moiré pattern removal for recaptured screen images
TL;DR: An unsupervised Generative Adversarial Network for Moire Removal (MR-GAN), which is the first attempt at unsuper supervised learning based moire removal, and outperforms state-of-the-art demoireing methods on a large set of test images.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Proceedings ArticleDOI
Image-to-Image Translation with Conditional Adversarial Networks
TL;DR: Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
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.
Proceedings ArticleDOI
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig,Lucas Theis,Ferenc Huszar,Jose Caballero,Andrew Cunningham,Alejandro Acosta,Andrew Peter Aitken,Alykhan Tejani,Johannes Totz,Zehan Wang,Wenzhe Shi +10 more
TL;DR: SRGAN as mentioned in this paper proposes a perceptual loss function which consists of an adversarial loss and a content loss, which pushes the solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.