-Architecture of Generative Adversarial Network (GAN)

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... generative model generates samples by passing random noise through a multilayer perceptron, and the discriminative model is also a multilayer perceptron. We can train both models using only the highly successful back propagation and dropout algorithms and sample from the generative model using only forward propagation. Fig. 4 shows the general architecture of GAN. To learn the generator's distribution í µí± í µí±” over data í µí±¥, we deeine a prior on input noise variables í µí± í µí± § (í µí± §), then represent a mapping to data space as í µí°º(í µí± §; í µí¼ƒ í µí±” ), where í µí°º is a differentiable function represented by a multilayer perceptron ...

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... discriminator distinguishes between real and fake images and helps the generator to produce more realistic images (Fig. 14). are more discriminative such as eyes, mouth and cheeks (Fig. 8). Fig. 9 shows the higher-level architectural design of multimodal adaptive normalization. The affine parameters i.e, scale, í µí»¾ and a shift, í µí»½ are typically used to learn the higher-order statistics of image features corresponding to style, texture, etc. to ...

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... generative model generates samples by passing random noise through a multilayer perceptron, and the discriminative model is also a multilayer perceptron. We can train both models using only the highly successful back propagation and dropout algorithms and sample from the generative model using only forward propagation. Fig. 4 shows the general architecture of GAN. To learn the generator's distribution í µí± í µí±” over data í µí±¥, we deeine a prior on input noise variables í µí± í µí± § (í µí± §), then represent a mapping to data space as í µí°º(í µí± §; í µí¼ƒ í µí±” ), where í µí°º is a differentiable function represented by a multilayer perceptron ...

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... discriminator distinguishes between real and fake images and helps the generator to produce more realistic images (Fig. 14). are more discriminative such as eyes, mouth and cheeks (Fig. 8). Fig. 9 shows the higher-level architectural design of multimodal adaptive normalization. The affine parameters i.e, scale, í µí»¾ and a shift, í µí»½ are typically used to learn the higher-order statistics of image features corresponding to style, texture, etc. to ...