Likelihood-based Out-of-Distribution Detection with Denoising Diffusion Probabilistic Models

• URL: http://arxiv.org/abs/2310.17432v1
• Date: Thu, 26 Oct 2023 14:40:30 GMT
• Title: Likelihood-based Out-of-Distribution Detection with Denoising Diffusion Probabilistic Models
• Authors: Joseph Goodier, Neill D.F. Campbell
• Abstract summary: We show that likelihood-based Out-of-Distribution detection can be extended to diffusion models. We propose a new likelihood ratio for Out-of-Distribution detection with Deep Denoising Diffusion Models.
• Score: 6.554019613111897
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Out-of-Distribution detection between dataset pairs has been extensively explored with generative models. We show that likelihood-based Out-of-Distribution detection can be extended to diffusion models by leveraging the fact that they, like other likelihood-based generative models, are dramatically affected by the input sample complexity. Currently, all Out-of-Distribution detection methods with Diffusion Models are reconstruction-based. We propose a new likelihood ratio for Out-of-Distribution detection with Deep Denoising Diffusion Models, which we call the Complexity Corrected Likelihood Ratio. Our likelihood ratio is constructed using Evidence Lower-Bound evaluations from an individual model at various noising levels. We present results that are comparable to state-of-the-art Out-of-Distribution detection methods with generative models.

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