通过调整输入熵来解决生成模型对异常数据误判问题。
Mitigating the Likelihood Paradox in Flow-based OOD Detection via Entropy Manipulation
- 基于语义相似度增强对异常输入的扰动强度
- 无需训练即可提升分布外检测准确率，平均提升1.8% AUROC
- 适合需要高可靠性异常检测的工业场景
基于流的生成模型虽能高效计算输入似然值，但常对分布外（OOD）输入赋予过高的似然值，形成似然悖论。本文提出通过控制输入熵缓解该问题：根据输入与内分布记忆库的语义相似度，对相似度低的样本施加更强扰动。理论分析表明，熵控制可增大内分布与分布外样本的期望对数似然差距，有利于内分布。该方法无需额外训练密度模型即可生效。在标准基准测试中，相比基线方法，本方法在多个数据集上均实现一致的AUROC提升，验证了其有效性。
原文摘要 · Abstract (English)
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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