Results 251 to 260 of about 5,739,313 (302)

Attention-Guided Global-Local Adversarial Learning for Detail-Preserving Multi-Exposure Image Fusion

IEEE transactions on circuits and systems for video technology (Print), 2022
Deep learning networks have recently demonstrated yielded impressive progress for multi-exposure image fusion. However, how to restore realistic texture details while correcting color distortion is still a challenging problem to be solved.
Jinyuan Liu, Jingjie Shang, Risheng Liu, Xin Fan   +3 more
semanticscholar   +1 more source

Deep Adversarial Metric Learning

IEEE Transactions on Image Processing, 2020
Learning an effective distance measurement between sample pairs plays an important role in visual analysis, where the training procedure largely relies on hard negative samples. However, hard negative samples usually account for the tiny minority in the training set, which may fail to fully describe the data distribution close to the decision boundary.
Yueqi Duan, Jiwen Lu, Wenzhao Zheng, Jie Zhou   +3 more
openaire   +2 more sources

Domain Adaptation With Multi-Adversarial Learning for Open-Set Cross-Domain Intelligent Bearing Fault Diagnosis

IEEE Transactions on Instrumentation and Measurement, 2023
Adversarial domain adaptation and transfer learning have been widely applied in the field of cross-domain fault diagnosis. However, the effectiveness of existing domain adaptation-based diagnostic methods relies on the assumption that both the source and
Zhixiao Zhu, Guangyi Chen, Gang Tang
semanticscholar   +1 more source

Adversarial Machine Learning

2023
This NIST AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is built on survey of the AML literature and is arranged in a conceptual hierarchy that includes key types of ML methods and lifecycle stage of attack, attacker goals and objectives, and attacker capabilities and ...
Ziv Katzir, Yuval Elovici
  +4 more sources

Multi-Modal Physiological Signals Based Squeeze-and-Excitation Network With Domain Adversarial Learning for Sleep Staging

IEEE Sensors Journal, 2022
Sleep staging is the basis of sleep medicine for diagnosing psychiatric and neurodegenerative diseases. However, the existing sleep staging methods ignore the fact that multi-modal physiological signals are heterogeneous, and different modalities ...
Ziyu Jia, Xiyang Cai, Zehui Jiao
semanticscholar   +1 more source

Triple Adversarial Learning and Multi-View Imaginative Reasoning for Unsupervised Domain Adaptation Person Re-Identification

IEEE transactions on circuits and systems for video technology (Print), 2022
Due to the importance of practical applications, unsupervised domain adaptation (UDA) person re-identification (re-ID) has attracted increasing attention.
Huafeng Li, Neng Dong, Zhengtao Yu, Dapeng Tao, Guanqiu Qi   +4 more
semanticscholar   +1 more source

APNet: Adversarial Learning Assistance and Perceived Importance Fusion Network for All-Day RGB-T Salient Object Detection

IEEE Transactions on Emerging Topics in Computational Intelligence, 2022
To improve the performance of salient object detection (SOD) in scenes with low-light conditions (e.g., nighttime) and cluttered backgrounds, infrared thermal images are used to supplement RGB images to achieve good all-day imaging as infrared images are
Wujie Zhou, Yun Zhu, Jingsheng Lei, Jian Wan, Lu Yu   +4 more
semanticscholar   +1 more source

Adversarial learning

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 2005
Many classification tasks, such as spam filtering, intrusion detection, and terrorism detection, are complicated by an adversary who wishes to avoid detection. Previous work on adversarial classification has made the unrealistic assumption that the attacker has perfect knowledge of the classifier [2].
Daniel Lowd, Christopher Meek
openaire   +1 more source

Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems

Knowledge Discovery and Data Mining, 2021
As an important means to solve information overload, recommender systems have been widely applied in many fields, such as e-commerce and advertising. However, recent studies have shown that recommender systems are vulnerable to poisoning attacks; that is,
Chenwang Wu, Defu Lian, Yong Ge, Zhihao Zhu, Enhong Chen   +4 more
semanticscholar   +1 more source

Flexible Body Partition-Based Adversarial Learning for Visible Infrared Person Re-Identification

IEEE Transactions on Neural Networks and Learning Systems, 2021
Person re-identification (Re-ID) aims to retrieve images of the same person across disjoint camera views. Most Re-ID studies focus on pedestrian images captured by visible cameras, without considering the infrared images obtained in the dark scenarios ...
Ziyu Wei, Xi Yang, Nannan Wang, Xinbo Gao   +3 more
semanticscholar   +1 more source