发表机构
University of Munich（慕尼黑大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何分离内容和风格表示，提出对比增强流匹配框架，将对比正则化融入可逆流匹配，在多个数据集实验中提升了内容和风格检索、增强嵌入簇分离及开集鲁棒性，改进了分布转移下的解缠和鲁棒性。
AI 中文摘要
学习分离内容和风格的表示对于可控生成和组合泛化至关重要。然而，主要基于生成目标训练的扩散和流模型常常产生纠缠或错位的因素。为解决这一差距，我们引入对比增强流匹配（CAtFM），将对比正则化集成到可逆流匹配公式中以促进结构化的内容-风格表示。在训练期间对预测端点应用对比监督，增强跨传输分布的语义一致性，使解缠隐式出现。在多个数据集上的实验表明，CAtFM 改进了内容和风格检索，增强了嵌入簇分离，在分布转移下实现了更强的开集鲁棒性。
英文摘要
Learning representations that separate content and style is crucial for controllable generation and compositional generalization. However, diffusion and flow-based models trained primarily with generative objectives often produce entangled or misaligned factors. To address this gap, we introduce Contrastive Augmented Flow Matching (CAtFM), a framework that integrates contrastive regularization into an invertible flow matching formulation to promote structured content-style representations. Rather than constraining intermediate latents or velocity fields, we apply contrastive supervision to predicted endpoints during training, enforcing semantic consistency across transported distributions while allowing disentanglement to emerge implicitly, without assuming strictly pure or fully factorized content and style representations. Our main experiments operate in the CLIP embedding space, with additional validation using frozen DINO and ALIGN encoders. Across synthetic data, in-domain styles, and real-world benchmarks (ImageNet, WikiArt, DomainNet, and DTD), CAtFM improves content and style retrieval, enhances embedding cluster separation, and achieves stronger open-set robustness compared to generative and discriminative baselines. Overall, CAtFM provides a simple way to couple discriminative constraints with deterministic transport, improving disentanglement and robustness under distribution shift.
Commentsunder review, code available at: https://github.com/CompVis/SCFlow/tree/main#-catfm-follow-up
