文件名称：Deep Convolutional Generative Adversarial Networks.md
文件大小：9KB
文件格式：MD
更新时间：2023-03-15 04:15:54
DCGAN
we introduced the basic ideas behind how GANs work. We showed that they can draw samples from some simple, easy-to-sample distribution, like a uniform or normal distribution, and transform them into samples that appear to match the distribution of some dataset. And while our example of matching a 2D Gaussian distribution got the point across, it is not especially exciting.