Using GAN's to generate dataset for CNN training I'm doing bachaleor thesis on traffic sign detection using single shot detector called YOLO. These single shot detectors can perform detection of objects in image and so they have specific way of training, ie. training on full images. Thats quite problem for me, because the biggest real dataset with full traffic sign images is Belgian one with 9000 images in 210 classes, which is unfortunately not enough to train good detector.

To overcome this problem, I've created DatasetGenerator, which does quite good job in generating synthetic datasets, you can see in the results directory.

Recently I came across GAN's which can (besides others) generate or extend existing dataset and I would like to use these networks to compare with my dataset generator. I've tried this introduction to GANs succesfully.

The problem is it's unsupervised learning and so there are no annotations. It means it's able to extend my dataset of traffic signs, but the generated dataset won't be annotated at all, which is problem.

So my question is: Is there any way how to use GAN's to extend my dataset of full traffic sign images with annotations of traffic sign class and position? Actually the class is not important, because I can do it separately for each class, but what matters is the position of traffic sign in generated image.
I think you'll enjoy this work from Apple on improving the realism of synthetic images.  Essentially what you need to do is generate a synthetic image then have your GAN modify the synthetic image so that a 1) a discriminator thinks it is real while also 2) not changing the gross structure of the image very much (so the traffic sign doesn't move) - yes, this loss function is going to take a little work!  

Making synthetic data realistic enough to allow models to generalize successfully in the real world is a very active and exciting area of research, not least with respect to robotics, and so the work you are doing now should make you very attractive indeed to the right employer.