Continuous Adversarial Flow Models
We propose continuous adversarial flow models, a type of continuous-time flow model trained with an adversarial objective. Unlike flow matching, which uses a fixed mean-squared-error criterion, our approach introduces a learned discriminator to guide training. This change in objective induces a different generalized distribution, which empirically produces samples that are better aligned with the target data distribution. Our method is primarily proposed for post-training existing flow-matching models, although it can also train models from scratch. On the ImageNet 256px generation task, our post-training substantially improves the guidance-free FID of latent-space SiT from 8.26 to 3.63 a...
| Authors | Shanchuan Lin, Ceyuan Yang, Zhijie Lin et al. |
|---|---|
| Primary category | cs.LG |
| Published | 13 April 2026 |
| Last updated at source | 25 August 2026 |
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