Adversarial training - Open Access .click

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On the existence of solutions to adversarial training in multiclass classification

European Journal of Applied Mathematics
Adversarial 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, Matt Jacobs, Jakwang Kim +2 more
doaj +1 more source

Smooth Adversarial Training

, 2020
tech ...
Xie, Cihang +4 more
openaire +2 more sources

From the Discovery of the Giant Magnetocaloric Effect to the Development of High‐Power‐Density Systems

Advanced Materials Technologies, EarlyView.
The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz +5 more
wiley +1 more source

A Two-Stage Adversarial Training Method Based on Stability Contrastive Learning to Enhance Adversarial Robustness

Applied Sciences
Neural network models are highly susceptible to adversarial sample attacks, causing significant differences in model predictions with even minor perturbations to the samples.
Wenjuan Ren, Zhanpeng Yang, Guangzuo Li
doaj +1 more source

On the Effectiveness of Adversarial Training in Defending against Adversarial Example Attacks for Image Classification

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

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

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 Informatics
Adversarial 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

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

adversarial attacks
deep learning
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adversarial robustness
adversarial attack
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data augmentation
deep neural network
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