Results 1 to 10 of about 60,338 (274)

3D Model Inpainting Based on 3D Deep Convolutional Generative Adversarial Network

IEEE Access, 2020
In recent years, the problem of hole repairing in the 3D model has been widely concerned in related fields. As the Generative Adversarial Network (GAN) has achieved great success in generating realistic images, a 3D mesh model repair method based on the ...
Xinying Wang, Dikai Xu, Fangming Gu
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MODIS Green-Tide Detection With a Squeeze and Excitation Oriented Generative Adversarial Network

IEEE Access, 2022
This paper presents a novel framework combining spectral analysis and machine learning for green-tide detection. The framework incorporates a squeeze and excitation (SE) attention module into a U-shaped generator of a generative adversarial network (GAN),
Xifang Jin, Yun Li, Jianhua Wan, Xinrong Lyu, Peng Ren, Jie Shang   +5 more
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Conditional Activation GAN: Improved Auxiliary Classifier GAN

IEEE Access, 2020
A conditional generative adversarial network (cGAN) is a generative adversarial network (GAN) that generates data with a desired condition from a latent vector.
Jeongik Cho, Kyoungro Yoon
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Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks

Big Data and Cognitive Computing, 2020
In this work, we propose a novel defense system against adversarial examples leveraging the unique power of Generative Adversarial Networks (GANs) to generate new adversarial examples for model retraining. To do so, we develop an automated pipeline using
Shayan Taheri, Aminollah Khormali, Milad Salem, Jiann-Shiun Yuan   +3 more
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Seismic random noise suppression using improved CycleGAN

Frontiers in Earth Science, 2023
Random noise adversely affects the signal-to-noise ratio of complex seismic signals in complex surface conditions and media. The primary challenges related to processing seismic data have always been reducing the random noise and increasing the signal-to-
Shimin Sun, Guihua Li, Renwei Ding, Lihong Zhao, Lihong Zhao, Yujie Zhang, Shuo Zhao, Jinwei Zhang, Junlin Ye   +8 more
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A Review of the Research and Development of Adversarial Generative Networks in Interior Graphic Design [PDF]

ITM Web of Conferences
This study provides a comprehensive overview of the research and development of adversarial generative networks in interior graphic design. With the continuous development of adversarial generative networks, the level of Generative Adversarial Networks ...
Yang Haonan
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House-GAN++: Generative Adversarial Layout Refinement Networks

, 2021
This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation. Our architecture is an integration of a graph-constrained relational GAN and a conditional GAN, where a previously generated layout becomes the next input constraint, enabling iterative refinement.
Nauata, Nelson, Hosseini, Sepidehsadat, Chang, Kai-Hung, Chu, Hang, Cheng, Chin-Yi, Furukawa, Yasutaka   +5 more
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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
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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
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Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs) [PDF]

IEEE Transactions on Cybernetics, 2021
Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e.
Cheng He, Shihua Huang, Ran Cheng, Kay Chen Tan, Yaochu Jin   +4 more
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