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After experiments, we demonstrate that Damage GAN outperforms state-of-the-art models, such as DCGAN and ContraD GAN, when applied to imbalanced datasets, thereby highlighting its potential for improving GAN performance on imbalanced datasets.
Dec 8, 2023 · Abstract:This study delves into the application of Generative Adversarial Networks (GANs) within the context of imbalanced datasets.
Dec 5, 2023 · The generator aims to learn the distribution of real samples, while the discriminator evaluates the authenticity of inputs, creating a dynamic “ ...
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Damage GAN: A Generative Model for Imbalanced Data - ResearchGate
www.researchgate.net › ... › Damage
Dec 8, 2023 · This study delves into the application of Generative Adversarial Networks (GANs) within the context of imbalanced datasets.
In this paper, we propose an improved GAN (I-GAN) to oversample according to the global underlying structure of minority samples. The originality of I-GAN stems ...
Missing: Damage | Show results with:Damage
Dec 21, 2023 · Abstract: Generative adversarial network (GAN) is an overwhelming yet promising method to address the data imbalance problem. However, most ...
Damage GAN: A Generative Model for Imbalanced Data. https://doi.org/10.1007 ... Class-balanced loss based on effective number of samples. In ...
May 5, 2021 · The generative model learns useful features to generate target minority-class samples. Compared with the state-of-the-art GAN model, the ...
Missing: Damage | Show results with:Damage
Generative adversarial networks for overlapped and imbalanced ...
www.researchgate.net › ... › Damage
May 23, 2024 · This study develops an oversampling method designed for rebalancing datasets in impact damage classification of reinforced concrete walls.
The oversampling technique is used in Generative Adversarial Networks (GANs) when the amount of collected or available data (sample size) is insufficient.
Missing: Damage | Show results with:Damage