I'd like to implement this paper:SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
They use a (W)GAN architecture for image segmentation. The generator part (called segmentor here) uses a U-Net like architecture, while the discriminator (here called critic) employs a novel multi-scale loss (essentially MAE over feature maps on different scales). I'd start out with something simple to get the code working (e.g. thresholded MNIST) and then move to more real-world data sets.
In the mean time I went ahead and implemented a SeGAN class including the training loop for the non-Wasserstein case. I got it working on MNIST and am testing it on production data. Once I am satisfied with it I might post some code.
Well, well. This sounds VERY interesting! I'm following your work, for sure, if you want to keep us updated!
Well, I got around to create a github repo. Please take a look and let me know what you did with it. Consider it a work in progress, naturally