Closed 89ng closed 3 years ago
Thank you for your interest in DoppelGANger! I understand that your question is about whether DoppelGANger would work for high-dimensional metadata. We did not test it for such high-dimension metadata you mentioned, so I don't have enough evidence to give you an accurate answer. On the high level what you plan to use DoppelGANGer makes sense to me, but I would suspect that you will need to change the network architecture and increase the model size a little bit. For example, the current metadata generator and discriminator are shallow MLPs, which I don't think will work well for images (i.e., the first case you said). Changing the metadata generator and discriminator to deeper CNNs or other more complicated architectures that people are using for images should give better results.
Hi Zinan,
Thank you so much for your patient reply! I will follow your suggestion and try to adjust the architecture a bit. I hope it finally works on my case and will report to you then haha.
Best,
Thank you so much for such great work! I have experimented with this novel GAN model and it works very well on some exemplary datasets. Whereas since my subsequent research capitalizing on synthetic data generation is mainly on urban local climate prediction, the types of attributes (metadata) seem to be very different, and I am really still a novice to the deep learning community especially regarding time series, so I would like to ask:
Since currently I need to prepare the training data according to the characteristics of DoppelGANer and it will be a very nasty and time-consuming process, I still could not answer these questions with my own experiments, sorry if I was too tedious with my case. Any suggestions or insights related to my questions will be appreciated. Thank you so much in advance!