Logit Mixture Outlier Exposure for Fine-grained Out-of-Distribution Detection
This work addresses the problem of robust out-of-distribution detection for machine learning models, which is incremental as it builds on existing methods like Outlier Exposure and Mixture Outlier Exposure.
The paper tackles the challenge of fine-grained out-of-distribution detection by proposing a linear interpolation technique in the logit space to mix in-distribution and out-of-distribution data, resulting in smoother model outputs and improved detection performance, particularly for data close to in-distribution boundaries.
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.