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.

翻译：数据集对之间的分布外检测已通过生成模型得到广泛探索。我们证明，基于似然的分布外检测可扩展至扩散模型，其关键在于这类模型与其他基于似然的生成模型一样，会显著受到输入样本复杂度的影响。目前，所有基于扩散模型的分布外检测方法均采用重建方式。我们提出了一种新的用于深度去噪扩散模型分布外检测的似然比，命名为复杂度校正似然比。该似然比通过单个模型在不同噪声水平下的证据下界评估构建而成。我们展示的结果与当前基于生成模型的最先进分布外检测方法性能相当。