Results 31 to 40 of about 3,042,897 (294)
Score-Guided Generative Adversarial Networks
Axioms, 2022 We propose a generative adversarial network (GAN) that introduces an evaluator module using pretrained networks. The proposed model, called a score-guided GAN (ScoreGAN), is trained using an evaluation metric for GANs, i.e., the Inception score, as a ...
Minhyeok Lee, Junhee Seok
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Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms
Applied Sciences, 2020 The fossil record is notorious for being incomplete and distorted, frequently conditioning the type of knowledge that can be extracted from it. In many cases, this often leads to issues when performing complex statistical analyses, such as classification
Lloyd A. Courtenay +1 more
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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
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LDDMM Meets GANs: Generative Adversarial Networks for Diffeomorphic Registration [PDF]
, 2021 In this work, we propose an unsupervised adversarial learning LDDMM method for 3D mono-modal images based on Generative Adversarial Networks.
Elvira Mayordomo Cámara +5 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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Correction to “Generative Adversarial Networks for Crystal Structure Prediction”
, 2022 Correction to “Generative Adversarial Networks for Crystal Structure ...
Sungwon Kim (242473) +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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Super‐resolution with adversarial loss on the feature maps of the generated high‐resolution image
Electronics Letters, 2022 Recent studies on image super‐resolution make use of Generative Adversarial Networks to generate the high‐resolution image counterpart of the low‐resolution input. However, while being able to generate sharp high‐resolution images, Generative Adversarial
I. Imanuel, S. Lee
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Sparse Generative Adversarial Network [PDF]
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019 We propose a new approach to Generative Adversarial Networks (GANs) to achieve an improved performance with additional robustness to its so-called and well recognized mode collapse. We first proceed by mapping the desired data onto a frame-based space for a sparse representation to lift any limitation of small support features prior to learning the ...
Shahin Mahdizadehaghdam +2 more
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