Catastrophic Compositional Generation: Why Vanilla Diffusion Models Fail to Extrapolate
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Computer Science > Machine Learning
Title:Catastrophic Compositional Generation: Why Vanilla Diffusion Models Fail to Extrapolate
Abstract:The task of compositional generation involves using a conditional generative model, trained only on a subset of the possible conditions, to produce samples from compositionally-defined target distributions such as a geometric combination of the source distributions. In this work, we argue that this task is often infeasible for vanilla conditional diffusion models: we conjecture that no inference-time technique can efficiently produce samples from the target distribution in certain well-motivated settings. This idea is supported by theory-guided generalization arguments and carefully-designed experiments on both synthetic and realistic data. In particular, while recent methods such as Feynman-Kac correction reduce inference-time approximation error, our results show that score estimation error has a more catastrophic effect on performance when the target distribution is out-of-distribution with respect to the sources, highlighting the need for a different approach to this task.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.6 |
| Cite as: | arXiv:2606.23920 [cs.LG] |
| (or arXiv:2606.23920v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.23920
arXiv-issued DOI via DataCite (pending registration)
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