Deep generative models, including diffusion and flow matching, have shown outstanding performance in synthesizing realistic multi-modal content across images, audio, video, and text. However, the generalization capabilities and underlying mechanisms of these models are challenging in deep generative modeling. The core challenge includes understanding whether generative models truly generalize and the mechanisms behind this generalization. In the case of flow matching models, it has been argued that generalization comes from approximation rather than stochasticity.

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