Probability Density from Latent Diffusion Models for Out-of-Distribution Detection
Joonas Järve, Karl Kaspar Haavel, Meelis Kull · Frontiers in artificial intelligence and applications · 2025
Despite rapid advances in AI, safety remains the main bottleneck to deploying machine-learning systems. A critical safety component is out-of-distribution (OOD) detection: given an input, decide whether it comes from the same distribution as the training data. In generative models, the most natural OOD score is the data likelihood. Actually, under the assumption of uniformly distributed OOD data, the likelihood is even the optimal OOD detector, as we show in this work. However, earlier work reported that likelihood often fails in practice, raising doubts about its usefulness. We explore if, in practice, the representation space also suffers from the inability to learn good density estimation for OOD detection, or if it is merely a problem of the pixel-space typically used in generative models. To test this, we trained a Variational Diffusion Model (VDM) not on images, but on the representation space of a pre-trained ResNet-18 to assess the performance of our likelihood-based-detector in comparison to the state-of-the-art methods from OpenOOD suite.