Results 21 to 30 of about 5,739,313 (302)
Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation [PDF]
Neural Information Processing Systems, 2022 Open-Set Domain Adaptation (OSDA) assumes that a target domain contains unknown classes, which are not discovered in a source domain. Existing domain adversarial learning methods are not suitable for OSDA because distribution matching with $\textit ...
Joonho Jang +5 more
semanticscholar +1 more source
Adversarial Malware Generation Method Based on Genetic Algorithm [PDF]
Jisuanji kexue, 2023 In recent years,with the development of Internet technology,malware has become an important method of network attack.To defend against malware attacks,deep learning techniques can be applied to malware detection.However,due to the limitations of deep ...
LI Kun, GUO Wei, ZHANG Fan, DU Jiayu, YANG Meiyue
doaj +1 more source
Adversarial Robustness of Deep Reinforcement Learning Based Dynamic Recommender Systems
Frontiers in Big Data, 2022 Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including interactive recommendation systems.
Siyu Wang +5 more
doaj +1 more source
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models [PDF]
IEEE International Conference on Computer Vision, 2019 Several recent works have shown how highly realistic human head images can be obtained by training convolutional neural networks to generate them. In order to create a personalized talking head model, these works require training on a large dataset of ...
Egor Zakharov +3 more
semanticscholar +1 more source
Adversarial Learning for Zero-Shot Stance Detection on Social Media [PDF]
North American Chapter of the Association for Computational Linguistics, 2021 Stance detection on social media can help to identify and understand slanted news or commentary in everyday life. In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topics ...
Emily Allaway +2 more
semanticscholar +1 more source
Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019 Perceptual ad-blocking is a novel approach that detects online advertisements based on their visual content. Compared to traditional filter lists, the use of perceptual signals is believed to be less prone to an arms race with web publishers and ad networks. We demonstrate that this may not be the case.
Tramèr, Florian +4 more
openaire +2 more sources
Adversarial Examples Detection Method Based on Image Denoising and Compression [PDF]
Jisuanji gongcheng, 2023 Numerous deep learning achievements in the field of computer vision have been widely applied in real life. However, adversarial examples can lead to false positives in deep learning models with high confidence, resulting in serious security consequences.
Feiyu WANG, Fan ZHANG, Jiayu DU, Hongle LEI, Xiaofeng QI
doaj +1 more source
Adversarial Learning for Neural Dialogue Generation [PDF]
Conference on Empirical Methods in Natural Language Processing, 2017 We apply adversarial training to open-domain dialogue generation, training a system to produce sequences that are indistinguishable from human-generated dialogue utterances.
Jiwei Li +5 more
semanticscholar +1 more source
Adversarial Continual Learning [PDF]
, 2020 Accepted at ECCV ...
Ebrahimi, Sayna +4 more
openaire +2 more sources
IEEE Access, 2023 Over the last decade, methods for autonomous control by artificial intelligence have been extensively developed based on deep reinforcement learning (DRL) technologies.
Kohei Ohashi +3 more
doaj +1 more source