MIMFlow：将掩码图像建模与归一化流集成用于端到端图像生成
MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation
AI总结：
提出MIMFlow框架，通过VAE编码器从掩码图像推断语义潜在变量，使归一化流专注于低频语义流形，解码器处理高频合成，解决NF容量瓶颈，在ImageNet 256×256上达到71.3%线性探测精度和2.50 FID。
摘要：
归一化流（NFs）是强大的生成模型，能够进行精确的密度估计和采样。然而，其严格的可逆性常常迫使模型将容量消耗在低层像素细节上，阻碍了对高层语义结构的捕获。尽管掩码图像建模（MIM）在表示学习方面表现出色，但其在生成流程中的集成仍然主要是模块化和分离的。在本文中，我们提出了MIMFlow，一个统一的端到端框架，联合优化潜在语义、像素重建和生成流。通过采用VAE编码器从掩码图像推断语义潜在变量，MIMFlow实现了生成任务的原则性解耦：归一化流专注于建模简化的低频语义流形，而专门的解码器处理高频合成。这种设计有效地解决了NF固有的容量瓶颈，使模型能够优先考虑全局结构一致性而非冗余噪声。在ImageNet 256×256上的实验结果表明，MIMFlow-L达到了71.3%的线性探测精度和2.50的FID。尽管仅使用了128个token（比标准模型少50%），它相比类似规模的NF基线获得了32.8%的性能提升。我们的代码可在以下网址获取：https://this URL。
英文摘要：
Normalizing Flows (NFs) are powerful generative models capable of exact density estimation and sampling. However, their strict invertibility often forces the model to exhaust its capacity on low-level pixel details, hindering the capture of high-level semantic structures. While Masked Image Modeling (MIM) has excelled in representation learning, its integration into generative pipelines has remained largely modular and disjointed. In this paper, we propose MIMFlow, a unified end-to-end framework that jointly optimizes latent semantics, pixel reconstruction, and generative flow. By employing a VAE encoder to infer semantic latent from masked images, MIMFlow achieves a principled decoupling of the generative task: the Normalizing Flow focuses on modeling a simplified, low-frequency semantic manifold, while a specialized decoder handles high-frequency synthesis. This design effectively resolves the inherent capacity bottleneck of NFs, allowing the model to prioritize global structural coherence over redundant noise. Empirical results on ImageNet 256$\times$256 show that MIMFlow-L reaches 71.3\% linear probing accuracy and an FID of 2.50. Despite using only 128 tokens (50\% fewer than standard models), it yields a 32.8\% performance gain over similar-scale NF baselines. Our code is available at https://github.com/MCG-NJU/MIMFlow.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
