Comprehensive OOD Detection Improvements
This work addresses the critical need for reliable OOD detection in machine learning applications, but it is incremental as it builds upon existing methods.
The paper tackles the problem of out-of-distribution (OOD) detection by improving both representation-based and logit-based methods, achieving state-of-the-art results on the OpenOODv1.5 benchmark with significant performance gains.
As machine learning becomes increasingly prevalent in impactful decisions, recognizing when inference data is outside the model's expected input distribution is paramount for giving context to predictions. Out-of-distribution (OOD) detection methods have been created for this task. Such methods can be split into representation-based or logit-based methods from whether they respectively utilize the model's embeddings or predictions for OOD detection. In contrast to most papers which solely focus on one such group, we address both. We employ dimensionality reduction on feature embeddings in representation-based methods for both time speedups and improved performance. Additionally, we propose DICE-COL, a modification of the popular logit-based method Directed Sparsification (DICE) that resolves an unnoticed flaw. We demonstrate the effectiveness of our methods on the OpenOODv1.5 benchmark framework, where they significantly improve performance and set state-of-the-art results.