Results 51 to 60 of about 39,370 (305)
Annealed Generative Adversarial Networks
CoRR, 2017 9 pages, 6 ...
Arash Mehrjou +2 more
openaire +2 more sources
Stacked Generative Adversarial Networks [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 CVPR 2017, camera-ready ...
Xun Huang 0002 +4 more
openaire +2 more sources
Recent Generative Adversarial Approach in Face Aging and Dataset Review
IEEE Access, 2022 Many studies have been conducted in the field of face aging, from approaches that use pure image-processing algorithms, to those that use generative adversarial networks.
Hady Pranoto +3 more
doaj +1 more source
, 2020 Developing new small molecules that are bioactive is time-consuming, costly and rarely successful. As a mitigation strategy, we apply, for the first time, generative adversarial networks to de novo design of small molecules using a phenotype-based drug ...
David, Rouquié +4 more
core +1 more source
Generative Adversarial Optical Networks Using Diffractive Layers for Digit and Action Generation
PhotonicsWithin the traditional electronic neural network framework, Generative Adversarial Networks (GANs) have achieved extensive applications across multiple domains, including image synthesis, style transfer and data augmentation.
Pei Hu +3 more
doaj +1 more source
Unrolled Generative Adversarial Networks
CoRR, 2016 We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator's objective, which is ideal but infeasible in practice, and using the current value of ...
Luke Metz +3 more
openaire +3 more sources
A progressive growing of conditional generative adversarial networks model
Dianxin kexue, 2023 Progressive growing of generative adversarial networks (PGGAN) is an adversarial network model that can generate high-resolution images.However, when the categories of samples are unbalanced, or the categories of samples are too similar or too dissimilar,
Hui MA, Ruiqin WANG, Shuai YANG
doaj +2 more sources
A generative adversarial network to Reinhard stain normalization for histopathology image analysis
Ain Shams Engineering JournalHistopathology image analysis is paramount importance for accurate diagnosing diseases and gaining insight into tissue properties. The significant challenge of staining variability continues.
Afnan M. Alhassan
doaj +1 more source
Triangle Generative Adversarial Networks
CoRR, 2017 A Triangle Generative Adversarial Network ($Δ$-GAN) is developed for semi-supervised cross-domain joint distribution matching, where the training data consists of samples from each domain, and supervision of domain correspondence is provided by only a few paired samples.
Zhe Gan +7 more
openaire +3 more sources
Graphical Generative Adversarial Networks
CoRR, 2018 We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data. Graphical-GAN conjoins the power of Bayesian networks on compactly representing the dependency structures among random variables and that of generative adversarial networks on learning expressive dependency functions.
Chongxuan Li +3 more
openaire +3 more sources