One Pass Is Not Enough: Recursive Latent Refinement for Generative Models

2026-05-14

AI Summary

Existing generative models often suffer from mode collapse when optimizing the Fréchet Inception Distance (FID), struggling to balance sample quality and diversity. This work proposes a Recursive Tuning Mechanism (RTM), which, for the first time, integrates iterative latent refinement into the generative process, replacing the single-pass forward mapping used in architectures like StyleGAN2. RTM significantly enhances mode coverage without compromising fidelity. By combining implicit maximum likelihood estimation (IMLE) with a precision-recall evaluation framework, RTM achieves state-of-the-art precision and recall on CIFAR-10, CelebA-HQ, and nine few-shot benchmarks while maintaining excellent FID scores. Moreover, it substantially outperforms StyleGAN2 and its ADA variant on high-resolution tasks such as AFHQ-v1.

Abstract

Despite remarkable progress, image generation is far from solved. The dominant metric, FID, conflates sample fidelity with mode coverage and is close to being saturated. Yet a model can still exhibit mode collapse while achieving a low FID, since a handful of sharp, near-duplicate images can outscore a model that faithfully covers the full data distribution. We argue that precision and recall are essential complements to FID, and that because FID is already saturated, the more meaningful goal is to improve diversity and coverage. Achieving high recall requires a model that explicitly prioritizes mode coverage, unlike most generative models, which optimize sample fidelity. We introduce RTM, which replaces the single-pass latent mapping in style-based generators with an iterative refinement process, and show that this consistently improves both quality and diversity. Integrated with Implicit Maximum Likelihood Estimation (IMLE), which optimizes mode coverage by design, RTM achieves the highest precision and recall among current state-of-the-art approaches while maintaining competitive FID, with improvements across CIFAR-10, CelebA-HQ at 256x256, and nine few-shot benchmarks. RTM also improves StyleGAN2 and StyleGAN2-ADA on CIFAR-10 and AFHQ-v1 at 512x512, demonstrating that the benefit is not specific to IMLE. Unlike flow-matching baselines that achieve competitive FID at the expense of coverage, recursive refinement improves both quality and diversity simultaneously.

Problem

• image generation
• mode collapse
• diversity
• recall
• FID

Innovation

• Recursive Latent Refinement
• Precision and Recall
• Mode Coverage
• Generative Models
• Iterative Refinement