Results 31 to 40 of about 4,829,196 (302)
Intervention Generative Adversarial Networks
CoRR, 2020 In this paper we propose a novel approach for stabilizing the training process of Generative Adversarial Networks as well as alleviating the mode collapse problem. The main idea is to introduce a regularization term that we call intervention loss into the objective.
Jiadong Liang +3 more
openaire +3 more sources
Multi‐style Chinese art painting generation of flowers
IET Image Processing, 2021 With the proposal and development of Generative Adversarial Networks, the great achievements in the field of image generation are made. Meanwhile, many works related to the generation of painting art have also been derived. However, due to the difficulty
Feifei Fu +3 more
doaj +1 more source
A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images
Remote Sensing, 2023 High-resolution images have a wide range of applications in image compression, remote sensing, medical imaging, public safety, and other fields. The primary objective of super-resolution reconstruction of images is to reconstruct a given low-resolution ...
Xuan Wang +3 more
doaj +1 more source
Progressive growing of generative adversarial network (GAN) (PGGAN) training.
, 2020 Progressive growing of generative adversarial network (GAN) (PGGAN) training.
Kazuyoshi Imaizumi (488891) +6 more
core +1 more source
Slimmable Generative Adversarial Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Generative adversarial networks (GANs) have achieved remarkable progress in recent years, but the continuously growing scale of models make them challenging to deploy widely in practical applications. In particular, for real-time generation tasks, different devices require generators of different sizes due to varying computing power.
Liang Hou +5 more
openaire +4 more sources
Generative Adversarial Networks in Retinal Image Classification
Applied Sciences, 2023 The recent introduction of generative adversarial networks has demonstrated remarkable capabilities in generating images that are nearly indistinguishable from real ones.
Francesco Mercaldo +4 more
doaj +1 more source
Sparse Generative Adversarial Network [PDF]
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019 We propose a new approach to Generative Adversarial Networks (GANs) to achieve an improved performance with additional robustness to its so-called and well recognized mode collapse. We first proceed by mapping the desired data onto a frame-based space for a sparse representation to lift any limitation of small support features prior to learning the ...
Shahin Mahdizadehaghdam +2 more
openaire +3 more sources
An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs
Applied Sciences, 2020 Medical image segmentation is a classic challenging problem. The segmentation of parts of interest in cardiac medical images is a basic task for cardiac image diagnosis and guided surgery.
Xiaochang Wu, Xiaolin Tian
doaj +1 more source
Adversarial Gaussian Denoiser for Multiple-Level Image Denoising
Sensors, 2021 Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian ...
Aamir Khan +4 more
doaj +1 more source
Structured Generative Adversarial Networks
CoRR, 2017 We study the problem of conditional generative modeling based on designated semantics or structures. Existing models that build conditional generators either require massive labeled instances as supervision or are unable to accurately control the semantics of generated samples.
Zhijie Deng +6 more
openaire +4 more sources