Mitigating the Likelihood Paradox in Flow-based OOD Detection via Entropy Manipulation

• URL: http://arxiv.org/abs/2602.09581v1
• Date: Tue, 10 Feb 2026 09:31:03 GMT
• Title: Mitigating the Likelihood Paradox in Flow-based OOD Detection via Entropy Manipulation
• Authors: Donghwan Kim, Hyunsoo Yoon,
• Abstract summary: We argue that entropy control increases the expected log-likelihood gap between in-distribution and OOD samples in favor of the in-distribution.<n>We then evaluate our method against likelihood-based OOD detectors on standard benchmarks and find consistent AUROC improvements over baselines.
• Score: 24.066271161451425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep generative models that can tractably compute input likelihoods, including normalizing flows, often assign unexpectedly high likelihoods to out-of-distribution (OOD) inputs. We mitigate this likelihood paradox by manipulating input entropy based on semantic similarity, applying stronger perturbations to inputs that are less similar to an in-distribution memory bank. We provide a theoretical analysis showing that entropy control increases the expected log-likelihood gap between in-distribution and OOD samples in favor of the in-distribution, and we explain why the procedure works without any additional training of the density model. We then evaluate our method against likelihood-based OOD detectors on standard benchmarks and find consistent AUROC improvements over baselines, supporting our explanation.

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