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Cumulative number of paper publications/journals related to GANs per year since its introduction in 2014

Cumulative number of paper publications/journals related to GANs per year since its introduction in 2014

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Generative adversarial networks (GANs) could be used efficiently for image and video generation, where labeled training data are available in bulk. In general, building a good machine learning model requires a reasonable amount of labeled training data. However, there are areas such that the biomedical field where the creation of such a data set is...

Contexts in source publication

Context 1
... Adversarial Networks (GANs) [6], established over the last few years, have attracted the attention of researchers (see Fig. 1). Several variants of GAN have been proposed for generating high-quality synthetic data, where they have been used for data augmentation in the case where traditional data augmentation methods do not yield good results. Thus, the feasibility of using GANs as a data augmentation technique to enhance classifier performance in GED ...
Context 2
... means that when new instances are generated using data augmentation techniques, it is important to ensure that the generated data is close to the original data, not only in terms of values but also in terms of data semantics. Generative Adversarial Networks (GANs) [6], established in recent years, have attracted the attention of researchers ( Fig. 1). Several variants of GANs have been proposed to generate high-quality synthetic data, where they have been used for data augmentation in cases where traditional data augmentation methods do not yield good results. ...

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