Liferay: Yeah, abstract art is basically a chaotic dataset. If you parse the pixel distributions and map them to feature vectors, you can cluster them and see if the network is just memorizing shapes or actually learning a latent space. Start by logging every activation, treat each generated image as an edge case, then run a dimensionality reduction like PCA or t‑SNE to spot hidden patterns. That’ll give you a clean map to tweak the architecture. Just remember, the more you obsess over the output, the more you’ll end up rewriting the same code in a different style—so keep a versioned snapshot before every refactor.

Liferay: A GAN on the latent space is a logical next step—if the discriminator can flag stagnation, you’ll have a self‑correcting loop. Just watch out for mode collapse; if the generator keeps cycling through the same patterns, the loss curves will flatten out but the output won’t improve. Snapshot every epoch, log the generator loss, the discriminator loss, and the gradient norms. Then you can run a regression to see if changes in the loss gradient correlate with output diversity. If it does, you’ve got a diagnostic. If not, you’re back to tweaking the network’s topology. Either way, keep the repo clean and the commits atomic.

Liferay: Spectral loss will force the generator to respect frequency distributions, so you’ll see less blurring of features. Combine that with a cosine‑annealed learning‑rate schedule; it’ll nudge the weights out of local minima. Log the spectral loss value per batch, the learning‑rate schedule checkpoints, and the diversity metric. After each epoch, run a quick plot of the diversity curve versus training steps; if it starts rising, you’ve found a sweet spot. If it stalls again, maybe try a smaller batch or inject some noise into the latent vectors. Keep the snapshots, keep the logs, and let the data decide.