Results 71 to 80 of about 237,731 (274)

Adversarial Example Detection and Classification With Asymmetrical Adversarial Training

, 2020
The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains.
Kolouri, Soheil, Rohde, Gustavo K., Yin, Xuwang   +2 more
core  

Stable Imitation of Multigait and Bipedal Motions for Quadrupedal Robots Over Uneven Terrains

Advanced Robotics Research, EarlyView.
How are quadrupedal robots empowered to execute complex navigation tasks, including multigait and bipedal motions? Challenges in stability and real‐world adaptation persist, especially with uneven terrains and disturbances. This article presents an imitation learning framework that enhances adaptability and robustness by incorporating long short‐term ...
Erdong Xiao, Yinzhao Dong, Ji Ma, Peng Lu   +3 more
wiley   +1 more source

Global Feature Attention Network: Addressing the Threat of Adversarial Attack for Aerial Image Semantic Segmentation

Remote Sensing, 2023
Aerial Image Semantic segmentation based on convolution neural networks (CNNs) has made significant process in recent years. Nevertheless, their vulnerability to adversarial example attacks could not be neglected.
Zhen Wang, Buhong Wang, Yaohui Liu, Jianxin Guo   +3 more
doaj   +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 Adversarial Attack via Penalty Method

IEEE Access
Deep learning systems have achieved significant success across various machine learning tasks. However, they are highly vulnerable to attacks. For example, adversarial examples can fool deep learning systems easily by perturbing inputs with small ...
Jiyuan Sun, Haibo Yu, Jianjun Zhao
doaj   +1 more source

EITGAN: A Transformation-based Network for recovering adversarial examples

Electronic Research Archive, 2023
Adversarial examples have been shown to easily mislead neural networks, and many strategies have been proposed to defend them. To address the problem that most transformation-based defense strategies will degrade the accuracy of clean images, we proposed
Junjie Zhao , Junfeng Wu, James Msughter Adeke, Guangjie Liu , Yuewei Dai   +4 more
doaj   +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

Adversarial attack and defense in reinforcement learning-from AI security view

Cybersecurity, 2019
Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV).
Tong Chen, Jiqiang Liu, Yingxiao Xiang, Wenjia Niu, Endong Tong, Zhen Han   +5 more
doaj   +1 more source

Foveation-based Mechanisms Alleviate Adversarial Examples [PDF]

, 2016
We show that adversarial examples, i.e., the visually imperceptible perturbations that result in Convolutional Neural Networks (CNNs) fail, can be alleviated with a mechanism based on foveations---applying the CNN in different image regions. To see this,
Boix, Xavier, Luo, Yan, Poggio, Tomaso, Roig, Gemma, Zhao, Qi   +4 more
core   +1 more source

CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions

Advanced Science, EarlyView.
CellPolaris decodes how transcription factors guide cell fate by building gene regulatory networks from transcriptomic data using transfer learning. It generates tissue‐ and cell‐type‐specific networks, identifies master regulators in cell state transitions, and simulates TF perturbations in developmental processes.
Guihai Feng, Xin Qin, Jiahao Zhang, Wuliang Huang, Yiyang Zhang, Wentao Cui, Yao Chen, Shirui Li, Wenhao Liu, Yao Tian, Yana Liu, Jingxi Dong, Ping Xu, Zhenpeng Man, Guole Liu, Zhongming Liang, Xinlong Jiang, Xiaodong Yang, Pengfei Wang, Ge Yang, Hongmei Wang, Xuezhi Wang, Ming‐Han Tong, Yuanchun Zhou, Shihua Zhang, Yiqiang Chen, Yong Wang, Xin Li   +27 more
wiley   +1 more source