Results 51 to 60 of about 215,342 (268)
On the existence of solutions to adversarial training in multiclass classification
European Journal of Applied MathematicsAdversarial training is a min-max optimization problem that is designed to construct robust classifiers against adversarial perturbations of data. We study three models of adversarial training in the multiclass agnostic-classifier setting.
Nicolás García Trillos +2 more
doaj +1 more source
Applied Sciences, 2020 State-of-the-art neural network models are actively used in various fields, but it is well-known that they are vulnerable to adversarial example attacks.
Sanglee Park, Jungmin So
doaj +1 more source
Multiple Adversarial Domains Adaptation Approach for Mitigating Adversarial Attacks Effects
International Transactions on Electrical Energy Systems, 2022 Although neural networks are near achieving performance similar to humans in many tasks, they are susceptible to adversarial attacks in the form of a small, intentionally designed perturbation, which could lead to misclassifications.
Bader Rasheed +4 more
doaj +1 more source
Domain Generalization via Adversarially Learned Novel Domains
IEEE Access, 2022 This study focuses on the domain generalization task, which aims to learn a model that generalizes to unseen domains by utilizing multiple training domains.
Yu Zhe +3 more
doaj +1 more source
Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space
Advanced Robotics Research, EarlyView.A quadruped robot masters dynamic jumps through constrained spaces with animal‐inspired moves and intelligent vision control. This hierarchical learning approach combines imitation of biological agility with real‐time trajectory planning. Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating ...
Zeren Luo +6 more
wiley +1 more source
ATVis: Understanding and diagnosing adversarial training processes through visual analytics
Visual InformaticsAdversarial training has emerged as a major strategy against adversarial perturbations in deep neural networks, which mitigates the issue of exploiting model vulnerabilities to generate incorrect predictions.
Fang Zhu +4 more
doaj +1 more source
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 +3 more
wiley +1 more source
Machine Learning and Knowledge ExtractionAdversarial training, a widely used technique for fortifying the robustness of machine learning models, has seen its effectiveness further bolstered by modifying loss functions or incorporating additional terms into the training objective.
Sander Joos +4 more
doaj +1 more source
Adaptive Density Estimation for Generative Models [PDF]
, 2019 Unsupervised learning of generative models has seen tremendous progress over recent years, in particular due to generative adversarial networks (GANs), variational autoencoders, and flow-based models.
Alahari, Karteek +4 more
core +2 more sources