Results 41 to 50 of about 6,306,959 (200)
Generalized Adversarially Learned Inference
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Allowing effective inference of latent vectors while training GANs can greatly increase their applicability in various downstream tasks. Recent approaches, such as ALI and BiGAN frameworks, develop methods of inference of latent variables in GANs by adversarially training an image generator along with an encoder to match two joint distributions of ...
Yatin Dandi +3 more
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DeepMal: maliciousness-Preserving adversarial instruction learning against static malware detection
Cybersecurity, 2021 Outside the explosive successful applications of deep learning (DL) in natural language processing, computer vision, and information retrieval, there have been numerous Deep Neural Networks (DNNs) based alternatives for common security-related scenarios ...
Chun Yang +6 more
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CoRR, 2018 Recently proposed adversarial training methods show the robustness to both adversarial and original examples and achieve state-of-the-art results in supervised and semi-supervised learning. All the existing adversarial training methods consider only how the worst perturbed examples (i.e., adversarial examples) could affect the model output.
Shufei Zhang +3 more
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Quantum Generative Adversarial Learning [PDF]
Physical Review Letters, 2018 5 pages, 1 ...
Lloyd, Seth, Weedbrook, Christian
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Adversarially Learned Inference
CoRR, 2016 We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the space of latent variables.
Vincent Dumoulin +6 more
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Learning Priors for Adversarial Autoencoders [PDF]
2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2018 Accepted by APSIPA ASC ...
Hui-Po Wang, Wei-Jan Ko, Wen-Hsiao Peng
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Clustering Approach for Detecting Multiple Types of Adversarial Examples
Sensors, 2022 With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model.
Seok-Hwan Choi +3 more
doaj +1 more source
CoRR, 2016 The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution.
Jeff Donahue +2 more
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Cycles in Adversarial Regularized Learning [PDF]
, 2018 22 pages, 4 ...
Mertikopoulos, Panayotis +2 more
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