Likelihood-based Out-of-Distribution Detection with Denoising Diffusion
  Probabilistic Models
Joseph Goodier Neill D. F. Campbell
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.