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• W4385361004 abstract "The most significant models of neuronal networks for unsupervised machine learning now include the generative adversarial networks (GAN) as a separate branch. They have recently shown some degree of success. However, there is still a major obstacle. Many different loss functions have been created to train GAN discriminators, and they all share a similar structure: a total amount of real and fake losses that are solely dependent on the real losses and the generated data respectively. A challenge with a sum equal to two losses is that the training may benefit from one loss while harming the other, as we shall demonstrate causes mode collapse and instability. First, we suggest that the current loss function may prevent learning fine details in the data while trying to contract the discriminator. In this paper, we provide a new class of loss discriminant functions that promotes the training of the discriminator with a higher frequency than that of the generator for make the discriminator more demanding compared to the generator and vice versa. We may choose appropriate weights to train the discriminator in a way that takes advantage of the GAN’s stability by using gradients of the real and fake loss parties. Our methodology may be used with any discriminator model where the loss is the total of true and fake pieces. Our approach entails appropriately adjusting the hyper-parameters in order to enhance the training of the two opposing models. The effectiveness of our loss functions on tasks involving the creation of curves and images has been demonstrated through experiments, improving the base results by a statistically significant margin on several datasets." @default.
• W4385361004 created "2023-07-29" @default.
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• W4385361004 date "2023-01-01" @default.
• W4385361004 modified "2023-09-23" @default.
• W4385361004 title "Effect of Convulsion Layers and Hyper-parameters on the Behavior of Adversarial Neural Networks" @default.
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• W4385361004 doi "https://doi.org/10.1007/978-3-031-39386-0_11" @default.
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