I found two projects on the internet. The first one is an interesting project, it tries to teach the model to generate a random icon of Homer Simpson style. 

And here is a gif showing its result from epoch 0 to epoch 300.

Here is a gallery of the final images it gets.

Another project is a useful and practical one, its title is ‘Quick and Easy Time Series Generation with Established Image-based GANs’. Then briefly search on the internet what time series is. In Wikipedia, it is described as a series of data points indexed (or graphed) in time order.

Examples:

So the purpose of this project is to generate this kind of graphs, but it also means that these datas do not have a concrete source and they are forged by the model.

But why we need to do that? It says that many surveys in scientific or financial areas need lots of datas while those datas are protected by the privacy of people. So the researcher cannot get abundant datasets. And then they may need this technique to get more useful data for their survey.

Here is its basic structure

Here is its final result and comparison with real time series

I have also tried the Big GAN Colab codes on my own and have generated these videos.