Results 101 to 110 of about 222,396 (351)

Enhanced Pathology Image Quality with Restore–Generative Adversarial Network [PDF]

, 2023
Ruichen Rong, Shidan Wang, Xinyi Zhang, Zhuoyu Wen, Xian Wu Cheng, Liwei Jia, Donghan M. Yang, Yang Xie, Xiaowei Zhan, Guanghua Xiao   +9 more
openalex   +1 more source

Information Transmission Strategies for Self‐Organized Robotic Aggregation

Advanced Robotics Research, EarlyView.
In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng, Zihan Li, Yifeng Shao, Jinhao Liu, Li Yu, Shuguang Li   +5 more
wiley   +1 more source

An End-to-End Conditional Random Fields and Skip-Connected Generative Adversarial Segmentation Network for Remote Sensing Images

Remote Sensing, 2019
Semantic segmentation is an important process of scene recognition with deep learning frameworks achieving state of the art results, thus gaining much attention from the remote sensing community.
Chu He, Peizhang Fang, Zhi Zhang, Dehui Xiong, Mingsheng Liao   +4 more
doaj   +1 more source

Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19 [PDF]

, 2023
Reza Kalantar, Sumeet Hindocha, Benjamin M. Hunter, Bhupinder Sharma, Nasir Khan, Dow‐Mu Koh, Merina Ahmed, Eric O. Aboagye, Richard W. Lee, Matthew Blackledge   +9 more
openalex   +1 more source

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

VAE-WACGAN: An Improved Data Augmentation Method Based on VAEGAN for Intrusion Detection

Sensors
To address the class imbalance issue in network intrusion detection, which degrades performance of intrusion detection models, this paper proposes a novel generative model called VAE-WACGAN to generate minority class samples and balance the dataset. This
Wuxin Tian, Yanping Shen, Na Guo, Jing Yuan, Yanqing Yang   +4 more
doaj   +1 more source

Training Generative Adversarial Networks With Weights [PDF]

2019 27th European Signal Processing Conference (EUSIPCO), 2019
The impressive success of Generative Adversarial Networks (GANs) is often overshadowed by the difficulties in their training. Despite the continuous efforts and improvements, there are still open issues regarding their convergence properties. In this paper, we propose a simple training variation where suitable weights are defined and assist the ...
Pantazis, Yannis, Paul, Dipjyoti, Fasoulakis, Michail, Stylianou, Yannis   +3 more
openaire   +2 more sources

Robust Physics‐Informed Neural Network Approach for Estimating Heterogeneous Elastic Properties from Noisy Displacement Data

Advanced Science, EarlyView.
Heterogeneous distributions of elasticity parameters are learned from noisy displacement data using the Inverse Elasticity Physics‐Informed Neural Network (IE‐PINN). This approach integrates both data‐driven and physics‐driven methods to address the significant limitations faced by existing techniques.
Tatthapong Srikitrungruang, Sina Aghaee Dabaghan Fard, Matthew Lemon, Jaesung Lee, Yuxiao Zhou   +4 more
wiley   +1 more source

Generative Adversarial Networks: Recent Developments [PDF]

, 2019
10 ...
Zamorski, Maciej, Zdobylak, Adrian, Zięba, Maciej, Świątek, Jerzy   +3 more
openaire   +2 more sources

High‐Asymmetry Metasurface: A New Solution for Terahertz Resonance via Active Learning‐Augmented Diffusion Model

Advanced Science, EarlyView.
A prior knowledge‐guided diffusion model augmented by physics‐constrained active learning is developed to design high‐asymmetry terahertz metamaterials. Trained on only a small set of classical structures, the model efficiently generates new high‐metrics designs.
Qiqi Dai, Yinpeng Wang, Cheng Xu, Dongxiao Li, Prakash Pitchappa, Thomas Caiwei Tan, Ranjan Singh, Chengkuo Lee   +7 more
wiley   +1 more source