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Exploration of the Relation between Input Noise and Generated Image in Generative Adversarial Networks

Journal of Electronic Science and Technology(2022)

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Abstract
In this paper, we propose a hybrid model aiming to map the input noise vector to the label of the generated image by the generative adversarial network (GAN). This model mainly consists of a pre-trained deep convolution generative adversarial network (DCGAN) and a classifier. By using the model, we visualize the distribution of two-dimensional input noise, leading to a specific type of the generated image after each training epoch of GAN. The visualization reveals the distribution feature of the input noise vector and the performance of the generator. With this feature, we try to build a guided generator (GG) with the ability to produce a fake image we need. Two methods are proposed to build GG. One is the most significant noise (MSN) method, and the other utilizes labeled noise. The MSN method can generate images precisely but with less variations. In contrast, the labeled noise method has more variations but is slightly less stable. Finally, we propose a criterion to measure the performance of the generator, which can be used as a loss function to effectively train the network.
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Key words
Deep convolution generative adversarial network (DCGAN),deep learning,guided generative adversarial network (GAN),visualization
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要点】:本文提出一种混合模型,通过映射输入噪声向量与生成图像标签的关系,旨在指导生成对抗网络(GAN)生成特定类型的图像,并创新性地提出了一种评估生成器性能的标准。

方法】:使用预训练的深度卷积生成对抗网络(DCGAN)与分类器组合的混合模型,通过可视化二维输入噪声分布,分析噪声向量与生成图像之间的关系。

实验】:通过两种方法构建指导生成器(GG):最显著噪声(MSN)方法和标记噪声方法。实验使用DCGAN生成图像,并观察不同训练阶段的噪声分布,最终提出了一种评估生成器性能的准则,作为训练网络的损失函数。实验结果未明确提及具体数据集名称。