Has anyone implemented the GAN unrolling procedure from https://arxiv.org/pdf/1611.02163v3.pdf?
Is the point that we simulate optimisation of the discriminator for K steps, then use the final error at D to optimise the parameters of D from before we started unrolling?
Does this mean we have to store a copy of D’s parameters before we start the unrolling procedure? Does this mean that we end up discarding all the optimisation changes that occurred during unrolling? (Excluding the fact that they are implicitly bundled into the final output from the unrolling).
Also, do we unroll for both real and fake data through D, or just fake data?
Note that one of the authors has a tensorflow implementation here (https://github.com/poolio/unrolled_gan/blob/master/Unrolled%20GAN%20demo.ipynb) but that hasn’t helped my understanding very much.
Any help with this would be very much appreciated, cheers!