| Deep Hybrid Models for Out-of-Distribution Detection | | 100 | 100 | | DHM | 2022-01-01 |
| Exploring the Limits of Out-of-Distribution Detection | ✓ Link | 98.52 | 97.75 | | R+ViT finetuned on CIFAR-10 | 2021-06-06 |
| Exploring the Limits of Out-of-Distribution Detection | ✓ Link | 98.42 | 97.68 | | ViT finetuned on CIFAR-10 | 2021-06-06 |
| Exploring the Limits of Out-of-Distribution Detection | ✓ Link | 97.85 | 96.28 | | MLP-Mixer finetuned on CIFAR-10 | 2021-06-06 |
| Forte : Finding Outliers with Representation Typicality Estimation | ✓ Link | 97.63 ± 00.15 | 09.69 ± 01.08 | | Forte | 2024-10-02 |
| Boosting Out-of-Distribution Detection with Multiple Pre-trained Models | ✓ Link | 97.12 | | 18.29 | ZODE-KNN | 2022-12-24 |
| Hybrid Models for Open Set Recognition | | 95.1 | | | OpenHybrid | 2020-03-27 |
| Semi-supervised novelty detection using ensembles with regularized disagreement | ✓ Link | 95.1 | | | ERD (ResNet18) | 2020-12-10 |
| Outlier Exposure with Confidence Control for Out-of-Distribution Detection | ✓ Link | 94.9 | 82.0 | | Wide 40-2 + OECC | 2019-06-08 |
| Deep Anomaly Detection with Outlier Exposure | ✓ Link | 93.3 | 76.2 | | WRN 40-2 + OE | 2018-12-11 |
| Out-of-Distribution Detection Using Outlier Detection Methods | ✓ Link | 91.95 | | | Isolation Forest on EfficientNet Softmax values | 2021-08-18 |
| Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty | ✓ Link | 90.9 | 67.7 | | WRN 40-2 + Rotation Prediction | 2019-06-28 |
| A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks | ✓ Link | 87.9 | 55.8 | | WRN 40-2 (MSP Baseline) | 2016-10-07 |
| Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices | ✓ Link | 79.7 | | | ResNet + diagonal elements of Gram matrix | 2019-12-28 |