Papers › Boosting Out-of-Distribution Detection with Multiple Pre-trained Models
Boosting Out-of-Distribution Detection with Multiple Pre-trained Models
Feng Xue, Zi He, Chuanlong Xie, Falong Tan, Zhenguo Li
Out-of-Distribution (OOD) detection, i.e., identifying whether an input is sampled from a novel distribution other than the training distribution, is a critical task for safely deploying machine learning systems in the open world. Recently, post hoc detection utilizing pre-trained models has shown promising performance and can be scaled to large-scale problems. This advance raises a natural question: Can we leverage the diversity of multiple pre-trained models to improve the performance of post hoc detection methods? In this work, we propose a detection enhancement method by ensembling multiple detection decisions derived from a zoo of pre-trained models. Our approach uses the p-value instead of the commonly used hard threshold and leverages a fundamental framework of multiple hypothesis testing to control the true positive rate of In-Distribution (ID) data. We focus on the usage of model zoos and provide systematic empirical comparisons with current state-of-the-art methods on various OOD detection benchmarks. The proposed ensemble scheme shows consistent improvement compared to single-model detectors and significantly outperforms the current competitive methods. Our method substantially improves the relative performance by 65.40% and 26.96% on the CIFAR10 and ImageNet benchmarks.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Out-of-Distribution Detection | CIFAR-10 | ZODE-KNN | AUROC | 99.12 | #6 of 10 | Archive leaderboard | report |
| Out-of-Distribution Detection | CIFAR-10 | ZODE-KNN | FPR95 | 3.83 | #6 of 10 | Archive leaderboard | report |
| Out-of-Distribution Detection | CIFAR-10 vs CIFAR-100 | ZODE-KNN | AUROC | 97.12 | #6 of 14 | Archive leaderboard | report |
| Out-of-Distribution Detection | CIFAR-10 vs CIFAR-100 | ZODE-KNN | FPR95 | 18.29 | #6 of 14 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections