联合图像-特征扩散中的共进化表示
Coevolving Representations in Joint Image-Feature Diffusion
AI总结：
本文提出CoReDi框架，通过共进化语义空间提升图像生成质量，实验显示其收敛更快且样本质量更高。
摘要：
联合图像-特征生成建模最近 emerged 作为一种有效策略，通过将低级VAE潜在编码与高级语义特征结合，提高扩散训练效果。然而，现有方法依赖固定表示空间，与生成目标无关且在训练过程中保持不变。我们主张表示空间应适应生成任务。为此，我们提出CoReDi框架，其中语义表示空间在训练过程中通过学习轻量级线性投影与扩散模型联合学习。虽然简单优化此投影会导致退化解，我们展示通过结合stop-gradient目标、归一化和针对性正则化可实现稳定共进化。此方法使语义空间逐步专门化以满足图像合成需求，提高其与图像潜在编码的互补性。我们将其应用于VAE潜在扩散和像素空间扩散，证明适应性语义表示在两种设置中提升生成建模效果。实验表明，CoReDi相比固定表示空间的联合扩散模型收敛更快且样本质量更高。
英文摘要：
Joint image-feature generative modeling has recently emerged as an effective strategy for improving diffusion training by coupling low-level VAE latents with high-level semantic features extracted from pre-trained visual encoders. However, existing approaches rely on a fixed representation space, constructed independently of the generative objective and kept unchanged during training. We argue that the representation space guiding diffusion should itself adapt to the generative task. To this end, we propose Coevolving Representation Diffusion (CoReDi), a framework in which the semantic representation space evolves during training by learning a lightweight linear projection jointly with the diffusion model. While naively optimizing this projection leads to degenerate solutions, we show that stable coevolution can be achieved through a combination of stop-gradient targets, normalization, and targeted regularization that prevents feature collapse. This formulation enables the semantic space to progressively specialize to the needs of image synthesis, improving its complementarity with image latents. We apply CoReDi to both VAE latent diffusion and pixel-space diffusion, demonstrating that adaptive semantic representations improve generative modeling across both settings. Experiments show that CoReDi achieves faster convergence and higher sample quality compared to joint diffusion models operating in fixed representation spaces.
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。
