如何使用新功能通过深度学习生成新图像

问题描述

如果我有一个由图像列表组成的数据集，每个图像都与一系列特征相关联；有一个模型，一旦训练，就可以在输入新的功能列表后生成新图像?

解决方案

我认为您正在寻找本文.

GAN是一种算法类型，它包含两个不同的模型，因此一个名为Discriminator的模型会尝试学习确定其输入数据是否来自数据集，而另一个名为Generator的模型则会尝试学习如何生成数据，因此辨别器错误地认识到它来自数据集.

您可以从以下链接中找到更多详细信息:

生成对抗网络(a)

生成对抗网络(GANs):引擎和应用程序

通过在Tensorflow后端上使用Keras的GAN实例

If i have a dataset consisting by a list of images each associated with a series of features; there is a model that, once trained, generates new images upon entering a new list of features?

解决方案

I think you are looking for GAN(Generative Adversarial Networks) which is proposed in this paper.

GAN are the type of algorithm which contains two different model so that one model named Discriminator tries to learn to determine if it's input data comes from the data set or not and the other one named Generator tries to learn how to generate data so that the Discriminator wrongly recognize that it comes from the data set.

You can find more details from the following links:

generative adversarial network (GAN)

Generative Adversarial Networks (GANs): Engine and Applications

GAN by Example using Keras on Tensorflow Backend

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