Abstract: We reveal an intriguing and prevalent phenomenon of diffusion models which we term as ``consistent model reproducibility'': given the same starting noise input and a deterministic sampler, different diffusion […]

Abstract: We reveal an intriguing and prevalent phenomenon of diffusion models which we term as "consistent model reproducibility'': given the same starting noise input and a deterministic sampler, different diffusion models often yield remarkably similar outputs while they generate new samples. We demonstrate this phenomenon through comprehensive experiments and theoretical studies, implying that different diffusion models consistently reach the same data distribution and scoring function regardless of frameworks, model architectures, or training procedures. More strikingly, our further investigation implies that diffusion models are learning distinct distributions affected by the training data size and model capacity, so that the model reproducibility manifests in two distinct training regimes with phase transition: (i) "memorization regime", where the diffusion model overfits to the training data distribution, and (ii) "generalization regime", where the model learns the underlying data distribution and generate new samples with finite training data. Finally, our results have strong practical implications regarding training efficiency, model privacy, and controllable generation of diffusion models, and our work raises numerous intriguing theoretical questions for future investigation.

Abstract: As the intelligence of everyday smart devices continues to evolve, they can already monitor basic health behaviors such as physical activities and heart rates. The vision of an intelligent […]

Abstract: "Modern database systems aim to support a large class of different use cases while simultaneously achieving high performance. However, as a result of their generality, databases often achieve adequate […]

HDSI! Come and join MathWorks Engineers for a technical seminar on AI (and lunch!) on Wednesday, April 3! Come learn why data scientists should learn MATLAB – we will highlight […]

Abstract: "In biomedical data analysis, an emerging trend focuses on contextualizing observations within biological and real-world processes. This approach facilitates high-resolution, context-specific insights by integrating information across datasets, but it […]

Abstract: Images are everywhere, especially images of the real world, and visual computing is important both for reconstructing useful models from these images and for providing us humans with interactive […]