Apr 15, 2020 · Learning the transformation which maps a low dimensional input noise to the data distribution forms the foundation for GANs.
Dec 5, 2021 · We conclude that the input noise dimension has a significant effect on the generation of images. To obtain useful and quality data generation, ...
We aim to quantitatively and qualitatively study the effect of the dimension of the input noise on the performance of GANs. For quantitative measures, typically ...
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Dec 10, 2020 · In Generative Adversarial Networks, the Generator takes noise vector as input and feeds it forward to create an image.
It is shown that the right dimension of input noise for optimal results depends on the data-set and architecture used, and further theoretical analysis is ...
Mar 12, 2019 · I can't find an explanation about how the shape of noise vector (Z) affects the Discriminator in the Generative Adversarial Networks.
Missing: Effect | Show results with:Effect
Learning the transformation which maps a low dimensional input noise to the data distribution forms the foundation for GANs. Despite their application in ...
Feb 1, 2024 · It's a function transforming some easy to sample from input distribution and trying to accurately map it to the obscenely complicated ...
Missing: Effect | Show results with:Effect
Mar 6, 2023 · Generally speaking, the optimal input dimensions for GANs depend on the type of data being used and the desired output. For example, for image ...
Mar 14, 2024 · [5] finds that the input noise dimension of GAN has significant impact on the sample's quality. However, this work is empirical without ...