Logit Mixture Outlier Exposure for Fine-grained Out-of-Distribution Detection
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Out-of-Distribution DetectionSimilar Papers 제목 키워드 기반
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments
Many real-world scenarios in which DNN-based recognition systems are deployed have inherently fine-grained attributes (e.g., bird-species recognition, medical image classification). In addition to achieving reliable accu…
image-classificationImage ClassificationMedical Image ClassificationOut-of-Distribution Detection+1RICASSO: Reinforced Imbalance Learning with Class-Aware Self-Supervised Outliers Exposure
In real-world scenarios, deep learning models often face challenges from both imbalanced (long-tailed) and out-of-distribution (OOD) data. However, existing joint methods rely on real OOD data, which leads to unnecessary…
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection
Detecting out-of-distribution (OOD) samples is essential when deploying machine learning models in open-world scenarios. Zero-shot OOD detection, requiring no training on in-distribution (ID) data, has been possible with…
Out-of-Distribution DetectionDiversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation
Out-of-distribution (OOD) detection is important for deploying reliable machine learning models on real-world applications. Recent advances in outlier exposure have shown promising results on OOD detection via fine-tunin…
Bayesian OOD detection with aleatoric uncertainty and outlier exposure
Typical Bayesian approaches to OOD detection use epistemic uncertainty. Surprisingly from the Bayesian perspective, there are a number of methods that successfully use aleatoric uncertainty to detect OOD points (e.g. Hen…
Out-of-Distribution DetectionOut of Distribution (OOD) Detection