Results 31 to 40 of about 45,910 (264)

Attention-Aware Generative Adversarial Networks (ATA-GANs) [PDF]

2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2018
In this work, we present a novel approach for training Generative Adversarial Networks (GANs). Using the attention maps produced by a Teacher- Network we are able to improve the quality of the generated images as well as perform weakly object localization on the generated images.
Kastaniotis, Dimitris, Ntinou, Ioanna, Tsourounis, Dimitrios, Economou, George, Fotopoulos, Spiros   +4 more
openaire   +2 more sources

SymReg-GAN: Symmetric Image Registration with Generative Adversarial Networks [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Symmetric image registration estimates bi-directional spatial transformations between images while enforcing an inverse-consistency. Its capability of eliminating bias introduced inevitably by generic single-directional image registration allows more precise analysis in different interdisciplinary applications of image registration, e.g., computational
Yuanjie, Zheng, Xiaodan, Sui, Yanyun, Jiang, Tongtong, Che, Shaoting, Zhang, Jie, Yang, Hongsheng, Li   +6 more
openaire   +2 more sources

Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential

Patterns, 2020
Summary: Image analysis in the field of digital pathology has recently gained increased popularity. The use of high-quality whole-slide scanners enables the fast acquisition of large amounts of image data, showing extensive context and microscopic detail
Maximilian E. Tschuchnig, Gertie J. Oostingh, Michael Gadermayr   +2 more
doaj   +1 more source

Pal-GAN: Palette-conditioned Generative Adversarial Networks

Journal of Computational Vision and Imaging Systems, 2021
Recent advances in Generative Adversarial Networks (GANs) have shown great progress on a large variety of tasks. A common technique used to yield greater diversity of samples is conditioning on class labels. Conditioning on high-dimensional structured or unstructured information has also been shown to improve generation results, e.g.
Graham Taylor, Adam Balint
openaire   +2 more sources

Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models

, 2018
Adversarial learning of probabilistic models has recently emerged as a promising alternative to maximum likelihood. Implicit models such as generative adversarial networks (GAN) often generate better samples compared to explicit models trained by maximum
Dhar, Manik, Ermon, Stefano, Grover, Aditya   +2 more
core   +1 more source

Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation

Applied Sciences, 2021
A synthetic image is a critical issue for computer vision. Traffic sign images synthesized from standard models are commonly used to build computer recognition algorithms for acquiring more knowledge on various and low-cost research issues. Convolutional
Christine Dewi, Rung-Ching Chen, Yan-Ting Liu, Hui Yu   +3 more
doaj   +1 more source

Generative Adversarial Optical Networks Using Diffractive Layers for Digit and Action Generation

Photonics
Within 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, Tengyu Cui, Yuanyuan Zhang, Shuai Feng   +3 more
doaj   +1 more source

Generative Adversarial Networks in Brain Imaging: A Narrative Review

Journal of Imaging, 2022
Artificial intelligence (AI) is expected to have a major effect on radiology as it demonstrated remarkable progress in many clinical tasks, mostly regarding the detection, segmentation, classification, monitoring, and prediction of diseases.
Maria Elena Laino, Pierandrea Cancian, Letterio Salvatore Politi, Matteo Giovanni Della Porta, Luca Saba, Victor Savevski   +5 more
doaj   +1 more source

Generative Target Tracking Method with Improved Generative Adversarial Network

IET Circuits, Devices and Systems, 2023
Multitarget tracking is prone to target loss, identity exchange, and jumping problems in the context of complex background, target occlusion, target scale, and pose transformation.
Yongping Yang, Hongshun Chen
doaj   +1 more source

Robustness of Generative Adversarial CLIPs Against Single-Character Adversarial Attacks in Text-to-Image Generation

IEEE Access
Generative Adversarial Networks (GANs) have emerged as a powerful type of generative model, particularly effective at creating images from textual descriptions.
Patibandla Chanakya, Putla Harsha, Krishna Pratap Singh   +2 more
doaj   +1 more source