Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations

• URL: http://arxiv.org/abs/2504.07793v1
• Date: Thu, 10 Apr 2025 14:30:41 GMT
• Title: Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations
• Authors: Yifan Ding, Arturas Aleksandrauskas, Amirhossein Ahmadian, Jonas Unger, Fredrik Lindsten, Gabriel Eilertsen,
• Abstract summary: Out-of-distribution (OOD) detection is critical for ensuring the reliability of deep learning systems.<n>Likelihood-based deep generative models have historically faced criticism for their unsatisfactory performance in OOD detection.<n>We show that likelihood-based methods can still perform on par with state-of-the-art methods when applied in the representation space of pre-trained encoders.
• Score: 16.317861186815364
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Out-of-distribution (OOD) detection is critical for ensuring the reliability of deep learning systems, particularly in safety-critical applications. Likelihood-based deep generative models have historically faced criticism for their unsatisfactory performance in OOD detection, often assigning higher likelihood to OOD data than in-distribution samples when applied to image data. In this work, we demonstrate that likelihood is not inherently flawed. Rather, several properties in the images space prohibit likelihood as a valid detection score. Given a sufficiently good likelihood estimator, specifically using the probability flow formulation of a diffusion model, we show that likelihood-based methods can still perform on par with state-of-the-art methods when applied in the representation space of pre-trained encoders. The code of our work can be found at $\href{https://github.com/limchaos/Likelihood-OOD.git}{\texttt{https://github.com/limchaos/Likelihood-OOD.git}}$.

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