Papers › Lipizzaner: A System That Scales Robust Generative Adversarial Network Training
Lipizzaner: A System That Scales Robust Generative Adversarial Network Training
Tom Schmiedlechner, Ignavier Ng Zhi Yong, Abdullah Al-Dujaili, Erik Hemberg, Una-May O'Reilly
GANs are difficult to train due to convergence pathologies such as mode and discriminator collapse. We introduce Lipizzaner, an open source software system that allows machine learning engineers to train GANs in a distributed and robust way. Lipizzaner distributes a competitive coevolutionary algorithm which, by virtue of dual, adapting, generator and discriminator populations, is robust to collapses. The algorithm is well suited to efficient distribution because it uses a spatial grid abstraction. Training is local to each cell and strong intermediate training results are exchanged among overlapping neighborhoods allowing high performing solutions to propagate and improve with more rounds of training. Experiments on common image datasets overcome critical collapses. Communication overhead scales linearly when increasing the number of compute instances and we observe that increasing scale leads to improved model performance.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections