用于细粒度分布外检测的Logit混合离群值暴露
Logit Mixture Outlier Exposure for Fine-grained Out-of-Distribution Detection
AI总结：
针对分布外检测中类间关系学习困难的问题，提出在logit空间进行线性插值混合分布内和分布外数据，并强制与输入空间混合的logit保持一致，以平滑决策边界并提升细粒度分布外检测性能。
摘要：
检测分布外数据的能力不仅对于确保模型对未知或意外输入数据的鲁棒性至关重要，而且对于提升模型的泛化性能也必不可少。在各种分布外检测方法中，Outlier Exposure和Mixture Outlier Exposure是有前景的方法，它们通过在训练期间暴露离群值数据来增强分布外检测性能。然而，即使采用这些复杂的技术，模型仍难以有效学习类间关系，并清晰区分来自分布内和分布外的数据采样。因此，我们关注logit空间，其中类间分布的属性与输入或特征空间中的属性明显分离。具体而言，我们提出了一种在logit空间中的线性插值技术，将分布内和分布外数据进行混合，以促进类间logit的平滑，并提高分布外检测性能，特别是对于接近分布内数据的分布外数据。此外，我们强制在logit空间中通过混合获得的logit与在输入空间中通过混合生成的logit之间保持一致性。我们的实验表明，我们的logit空间混合技术减少了模型输出在决策边界附近的剧烈波动，导致分布内和分布外数据之间更平滑、更可靠的分离。此外，我们在细粒度分布外检测任务上评估了所提方法的有效性。
英文摘要：
The ability to detect out-of-distribution data is essential not only for ensuring robustness against unknown or unexpected input data but also for improving the generalization performance of the model. Among various out-of-distribution detection methods, Outlier Exposure and Mixture Outlier Exposure are promising approaches that enhance out-of-distribution detection performance by exposing the outlier data during training. However, even with these sophisticated techniques, it remains challenging for models to learn the relationships between classes effectively and to distinguish data sampling from in-distribution and out-of-distribution clearly. Therefore, we focus on the logit space, where the properties between class-wise distributions are distinctly separated from those in the input or feature spaces. Specifically, we propose a linear interpolation technique in the logit space that mixes in-distribution and out-of-distribution data to facilitate smoothing logits between classes and improve the out-of-distribution detection performance, particularly for out-of-distribution data that lie close to the in-distribution data. Additionally, we enforce consistency between the logits obtained through mixing in the logit space and those generated via mixing in the input space. Our experiments demonstrate that our logit-space mixing technique reduces the abrupt fluctuations in the model outputs near the decision boundaries, resulting in smoother and more reliable separation between in-distribution and out-of-distribution data. Furthermore, we evaluate the effectiveness of the proposed method on a fine-grained out-of-distribution detection task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
