Results 91 to 100 of about 39,370 (305)

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
doaj   +1 more source

Solid Harmonic Wavelet Bispectrum for Image Analysis

Advanced Science, EarlyView.
The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown, Mathilda Avirett‐Mackenzie, Carolin Villforth, Georgios Exarchakis   +3 more
wiley   +1 more source

NAG: Network for Adversary Generation [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
Adversarial perturbations can pose a serious threat for deploying machine learning systems. Recent works have shown existence of image-agnostic perturbations that can fool classifiers over most natural images. Existing methods present optimization approaches that solve for a fooling objective with an imperceptibility constraint to craft the ...
Konda Reddy Mopuri, Utkarsh Ojha, Utsav Garg, R. Venkatesh Babu   +3 more
openaire   +2 more sources

Tympanic Membrane Generation with Generative Adversarial Networks

, 2021
© 2021 IEEE.Obtaining sufficient original data in most studies in the field of medical pattern recognition is a difficult and time consuming process. Different data augmentation methods are used to increase the amount of data to be used to train these ...
M. Elif Karsligil, Mustafa Furkan Eseoglu, Kocak, Ismail, KARSLIGİL YAVUZ, Mine Elif, Ismail Kocak, Eseoglu, Mustafa Furkan   +5 more
core   +1 more source

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

Advanced Science, EarlyView.
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang   +6 more
wiley   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Optoelectronic generative adversarial networks

Communications Physics
Recent breakthroughs in artificial intelligence generative content technology are driving transformational change. Diffractive optical networks offer a promising solution for high-speed, low-power generative models.
Jumin Qiu, Ganqing Lu, Tingting Liu, Dejian Zhang, Shuyuan Xiao, Tianbao Yu   +5 more
doaj   +1 more source

Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling [PDF]

, 2017
We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convo-lutional networks and ...
Wu, Jiajun, Freeman, William T., Zhang, Chengkai, Xue, Tianfan, Tenenbaum, Joshua B.   +4 more
core  

Improving Generative Adversarial Networks with Image Quality Assessment

, 2021
The research to find new ways to improve Generative Adversarial Networks (GANs) and ways to evaluate the data they produce is quite active. However, approaches to directly using those evaluation steps to improve Generative Adversarial Networks are quite ...
Perkins-Ollila, Justin W.
core  

Generation of novel Diels–Alder reactions using a generative adversarial network

, 2022
Deep learning has enormous potential in the chemical and pharmaceutical fields, and generative adversarial networks (GANs) in particular have exhibited remarkable performance in the field of molecular generation as generative models.
Yejian, Wu, Lan, Tang, Li, Sheng, Hongliang, Duan, Xinqiao, Wang, Wang, Xinqiao, Duan, Hongliang, Wu, Yejian, Tang, Lan, Sheng, Li   +9 more
core   +1 more source