Input complexity and out-of-distribution detection with likelihood-based generative models

Understand

Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system.

• However, likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significantly differ from training data.
• In this paper, we pose that this problem is due to the excessive influence that input complexity has in generative models' likelihoods.
• We report a set of experiments supporting this hypothesis, and use an estimate of input complexity to derive an efficient and parameter-free OOD score, which can be seen as a likelihood-ratio, akin to Bayesian model comparison.