Results 31 to 40 of about 2,921,969 (298)
Improving Generative Adversarial Networks with Image Quality Assessment
, 2021 The research to find new ways to improve Generative Adversarial Networks (GANs) and ways to evaluate the data they produce is quite active. However, approaches to directly using those evaluation steps to improve Generative Adversarial Networks are quite ...
Perkins-Ollila, Justin W.
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Hyperbolic Generative Adversarial Network
CoRR, 2021 Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning have gained popularity because of their ability to represent hierarchical data. We propose that it is possible to take advantage of the hierarchical characteristic present in the images by using hyperbolic neural networks in a GAN architecture.
Diego Lazcano +2 more
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Applied Sciences, 2023 Advancements in technology have improved human well-being but also enabled new avenues for criminal activities, including digital exploits like deep fakes, online fraud, and cyberbullying.
Claudio Navar Valdebenito Maturana +2 more
doaj +1 more source
Dynamics of Fourier Modes in Torus Generative Adversarial Networks
, 2021 Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution.
González Prieto, José Ángel +3 more
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Intervention Generative Adversarial Networks
CoRR, 2020 In this paper we propose a novel approach for stabilizing the training process of Generative Adversarial Networks as well as alleviating the mode collapse problem. The main idea is to introduce a regularization term that we call intervention loss into the objective.
Jiadong Liang +3 more
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Adversarial Learning in Accelerometer Based Transportation and Locomotion Mode Recognition
, 2022 This chapter demonstrates how adversarial learning can be used in the mobile computing domain. Specifically, we address the problem of improving the recognition of human activities from smartphone sensors, when limited training data is available ...
Wang, L +4 more
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Cell Morphology-Guided De Novo Hit Design by Conditioning Generative Adversarial Networks on Phenotypic Image Features [PDF]
, 2020 Developing new small molecules that are bioactive is time-consuming, costly and rarely successful. As a mitigation strategy, we apply, for the first time, generative adversarial networks to de novo design of small molecules using a phenotype-based drug ...
David, Rouquié +4 more
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A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity
IEEE Access, 2020 Machine learning algorithms are widely utilized in cybersecurity. However, recent studies show that machine learning algorithms are vulnerable to adversarial examples.
Sicong Zhang, Xiaoyao Xie, Yang Xu
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Exploring generative adversarial networks and adversarial training
International Journal of Cognitive Computing in Engineering, 2022 Recognized as a realistic image generator, Generative Adversarial Network (GAN) occupies a progressive section in deep learning. Using generative modeling, the underlying generator model learns the real target distribution and outputs fake samples from ...
Afia Sajeeda, B M Mainul Hossain, Ph.D
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Effective and Robust Boundary-Based Outlier Detection Using Generative Adversarial Networks
, 2022 Outlier detection aims to identify samples that do not match the expected patterns or major distribution of the dataset. It has played an important role in many domains such as credit card fraud identification, network intrusion detection, medical image ...
Liang Chang +11 more
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