🤖 AI Summary
This study addresses the training instability, texture drift, and artifact generation inherent in CycleGAN by proposing an enhanced framework integrating Wasserstein loss with gradient penalty, perceptual loss, multi-scale discriminators, and self-attention mechanisms. Innovatively, this work categorizes these components based on computational overhead during training versus inference to establish a resource-aware deployment prioritization strategy, specifically recommending deferred adoption of self-attention. Evaluated on horse-to-zebra translation, the proposed model significantly improves FID and KID metrics while effectively mitigating training collapse and reconstruction artifacts. Consequently, this research provides both theoretical foundations and practical guidelines for optimizing generative models in resource-constrained scenarios, balancing performance gains with computational efficiency through strategic component scheduling.
📝 Abstract
Teams that adopt cycle-consistent adversarial networks for unpaired image-to-image translation meet the same obstacles: adversarial training oscillates or collapses, cycle consistency preserves coarse layout while finer texture drifts, and a single discriminator judging global realism misses local artifacts. Four enhancements address these failures, and they are usually compared on output quality alone. We show that they also divide sharply by where their cost falls, and that this division, which follows from the architecture and not from any particular run, yields an adoption order for teams under a compute or latency budget. A Wasserstein objective with gradient penalty, a VGG19 perceptual loss on the cycle reconstruction, and multi-scale discriminators change training only, so a team can adopt or drop them without altering what ships. Self-attention alone persists into the deployed generator, with memory growing as the square of the feature-map size, which makes it the one component a resource-constrained team should defer. We integrate all four onto a lightly tuned baseline for horse-to-zebra translation, introduced one at a time on a fixed control and then combined, and for each we give the failure mode it targets and how it integrates. We document the collapse and reconstruction-artifact modes the baseline produced, report what visual inspection of saved samples showed for each variant, and report Fréchet Inception Distance and Kernel Inception Distance for the combined model. We specify the protocol still needed, covering the individual variants, perceptual similarity, and downstream segmentation, to rank these enhancements on measured evidence.