Mitigating the Likelihood Paradox in Flow-based OOD Detection via Entropy Manipulation
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866915788344000512 |
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| author | Kim, Donghwan Yoon, Hyunsoo |
| author_facet | Kim, Donghwan Yoon, Hyunsoo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_09581 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mitigating the Likelihood Paradox in Flow-based OOD Detection via Entropy Manipulation Kim, Donghwan Yoon, Hyunsoo Machine Learning Artificial Intelligence 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. |
| title | Mitigating the Likelihood Paradox in Flow-based OOD Detection via Entropy Manipulation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2602.09581 |