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

• URL: http://arxiv.org/abs/2605.15309v1
• Date: Thu, 14 May 2026 18:22:44 GMT
• Title: One Pass Is Not Enough: Recursive Latent Refinement for Generative Models
• Abstract summary: We argue that precision and recall are essential complements to FID.<n>We introduce RTM, which replaces the single-pass latent mapping in style-based generators with an iterative refinement process.<n>We show that RTM achieves the highest precision and recall among current state-of-the-art approaches.
• Score: 9.962560840358995
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Rarity-Aware Discrete Diffusion with Spatially Consistent Decoding for Photo-Realistic Image Super-Resolution [80.28905986756685]
  DiMOO-SR is a rarity-aware multimodal discrete diffusion framework for photo-realistic image Super-Resolution.<n>Experiments on widely used real-world SR benchmarks demonstrate that DiMOO-SR achieves competitive perceptual quality with only a few parallel decoding steps.
  arXiv  Detail & Related papers  (2026-07-20T07:01:01Z)
• IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder [74.25043153401586]
  We propose Ideal, an In-depth Alignment framework for discrete representation autoencoding.<n>By aligning quantized tokens with both shallow and deep VFM features, Ideal enables the resulting discrete visual tokens to preserve both visual fidelity and rich semantics.
  arXiv  Detail & Related papers  (2026-06-09T16:53:30Z)
• Policy-based Tuning of Autoregressive Image Models with Instance- and Distribution-Level Rewards [16.135177543347773]
  Autoregressive (AR) models are highly effective for image generation, yet their standard maximum-likelihood estimation training lacks direct optimization for sample quality and diversity.<n>We propose a lightweight RL framework that casts token-based AR inference as a Markov Decision Process, optimized via Group Relative Policy Optimization.<n>Our core contribution is the introduction of a novel distribution-level Leave-One-Out FID (LOO-FID) reward.
  arXiv  Detail & Related papers  (2026-03-24T11:28:36Z)
• Geometric Autoencoder for Diffusion Models [16.699694260098948]
  Latent diffusion models have established a new state-of-the-art in high-resolution visual generation.<n>We propose Geometric Autoencoder (GAE), a principled framework that unifies semantic discriminability, fidelity, and compactness.<n>GAE achieves compelling performance on the ImageNet-1K $256 times 256$ benchmark, reaching a gFID of 1.82 at only 80 epochs and 1.31 at 800 epochs.
  arXiv  Detail & Related papers  (2026-03-11T03:29:16Z)
• Improving Progressive Generation with Decomposable Flow Matching [50.63174319509629]
  Decomposable Flow Matching (DFM) is a simple and effective framework for the progressive generation of visual media.<n>On Imagenet-1k 512px, DFM achieves 35.2% improvements in FDD scores over the base architecture and 26.4% over the best-performing baseline.
  arXiv  Detail & Related papers  (2025-06-24T17:58:02Z)
• Randomized Autoregressive Visual Generation [26.195148077398223]
  This paper presents Randomized AutoRegressive modeling (RAR) for visual generation. RAR sets a new state-of-the-art performance on the image generation task while maintaining full compatibility with language modeling frameworks. On the ImageNet-256 benchmark, RAR achieves an FID score of 1.48, not only surpassing prior state-the-art autoregressive image generators but also outperforming leading diffusion-based and masked transformer-based methods.
  arXiv  Detail & Related papers  (2024-11-01T17:59:58Z)
• Model Inversion Attacks Through Target-Specific Conditional Diffusion Models [54.69008212790426]
  Model inversion attacks (MIAs) aim to reconstruct private images from a target classifier's training set, thereby raising privacy concerns in AI applications. Previous GAN-based MIAs tend to suffer from inferior generative fidelity due to GAN's inherent flaws and biased optimization within latent space. We propose Diffusion-based Model Inversion (Diff-MI) attacks to alleviate these issues.
  arXiv  Detail & Related papers  (2024-07-16T06:38:49Z)
• Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion [56.38386580040991]
  Consistency Trajectory Model (CTM) is a generalization of Consistency Models (CM) CTM enables the efficient combination of adversarial training and denoising score matching loss to enhance performance. Unlike CM, CTM's access to the score function can streamline the adoption of established controllable/conditional generation methods.
  arXiv  Detail & Related papers  (2023-10-01T05:07:17Z)
• Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows [54.050498411883495]
  We develop a novel training method for generative models, such as Generative Adversarial Networks and Normalizing Flows. We show that achieving a specified precision-recall trade-off corresponds to minimizing a unique $f$-divergence from a family we call the textitPR-divergences. Our approach improves the performance of existing state-of-the-art models like BigGAN in terms of either precision or recall when tested on datasets such as ImageNet.
  arXiv  Detail & Related papers  (2023-05-30T10:07:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.