arXiv:2606.29110v1 Announce Type: new Abstract: Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, there is a lack of similar advances in estimating the model likelihood. In particular, most existing methods either rely on restrictive architectures that enable exact calculations, or use stochastic approximations such as Hutchinson's trace estimator that introduce substantial variance. In this work, we introduce SCAlable LikeLihood distillation of flOw maPs (SCALLOP).
The continuous push for more efficient and scalable generative AI models is driving innovation in likelihood estimation, moving beyond restrictive architectures or stochastic approximations.
Improved likelihood estimation is crucial for developing generative AI models that are not only high-quality but also verifiable and predictable, expanding their practical applications.
The introduction of SCALLOP suggests a more robust and scalable approach to evaluating generative models, potentially leading to more reliable and deployable AI systems.
- · AI researchers
- · Generative AI developers
- · Machine learning startups
- · Industries using generative AI
- · Methods relying on computationally expensive likelihood estimations
- · AI models with unquantifiable uncertainty
- · Companies with less efficient generative model evaluation pipelines
More accurate and efficient likelihood estimation will improve the performance and trustworthiness of generative AI models.
This could accelerate the adoption of generative AI in critical applications where reliability and predictability are paramount.
Enhanced model evaluation could lead to the development of novel AI systems currently constrained by the difficulty of assessing model quality.
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Read at arXiv cs.LG