Results 51 to 60 of about 6,306,959 (200)
Domain Adversarial Reinforcement Learning
CoRR, 2021 We consider the problem of generalization in reinforcement learning where visual aspects of the observations might differ, e.g. when there are different backgrounds or change in contrast, brightness, etc. We assume that our agent has access to only a few of the MDPs from the MDP distribution during training.
Bonnie Li +3 more
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Anomaly-Based Intrusion on IoT Networks Using AIGAN-a Generative Adversarial Network
IEEE Access, 2023 Adversarial attacks have threatened the credibility of machine learning models and cast doubts over the integrity of data. The attacks have created much harm in the fields of computer vision, and natural language processing.
Zhipeng Liu +5 more
doaj +1 more source
Domain Invariant Adversarial Learning
CoRR, 2021 The phenomenon of adversarial examples illustrates one of the most basic vulnerabilities of deep neural networks. Among the variety of techniques introduced to surmount this inherent weakness, adversarial training has emerged as the most effective strategy for learning robust models.
Matan Levi +2 more
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High-fidelity audio generation and representation learning with guided adversarial autoencoder [PDF]
, 2020 Generating high-fidelity conditional audio samples and learning representation from unlabelled audio data are two challenging problems in machine learning research.
Rana, Rajib +3 more
core +1 more source
A Survey on Efficient Methods for Adversarial Robustness
IEEE Access, 2022 Deep learning has revolutionized computer vision with phenomenal success and widespread applications. Despite impressive results in complex problems, neural networks are susceptible to adversarial attacks: small and imperceptible changes in input space ...
Awais Muhammad, Sung-Ho Bae
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Addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives
, 2022 Smart healthcare systems are gaining popularity with the rapid development of intelligent sensors, the Internet of Things (IoT) applications and services, and wireless communications.
Jadidi, Z, Pal, S, Selvakkumar, A
core +1 more source
Adversarially Robust Learning with Tolerance
CoRR, 2022 We initiate the study of tolerant adversarial PAC-learning with respect to metric perturbation sets. In adversarial PAC-learning, an adversary is allowed to replace a test point $x$ with an arbitrary point in a closed ball of radius $r$ centered at $x$.
Hassan Ashtiani +2 more
openaire +3 more sources
Multiple Classifier Systems in Adversarial Environments: "Challenges and Solutions" [PDF]
, 2009 Pattern recognition methods offer technological background for a variety of applications in a modern information society. They are however undermined by several kinds of "adversarial" misuses like email and web spam, attacks to computer networks, etc.
Gargiulo, Francesco
core +1 more source
Adversarial consistency and the uniqueness of the adversarial bayes classifier
European Journal of Applied MathematicsMinimizing an adversarial surrogate risk is a common technique for learning robust classifiers. Prior work showed that convex surrogate losses are not statistically consistent in the adversarial context – or in other words, a minimizing sequence of the ...
Natalie S. Frank
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Adversarial robustness in deep learning: attacks on fragile neurons [PDF]
, 2021 We identify fragile and robust neurons of deep learning architectures using nodal dropouts of the first convolutional layer. Using an adversarial targeting algorithm, we correlate these neurons with the distribution of adversarial attacks on the network.
Martino, Ivan, +11 more
core +1 more source