ODP-Bench: Benchmarking Out-of-Distribution Performance Prediction
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915588470734848 |
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| author | Yu, Han Li, Kehan Li, Dongbai He, Yue Zhang, Xingxuan Cui, Peng |
| author_facet | Yu, Han Li, Kehan Li, Dongbai He, Yue Zhang, Xingxuan Cui, Peng |
| contents | Recently, there has been gradually more attention paid to Out-of-Distribution (OOD) performance prediction, whose goal is to predict the performance of trained models on unlabeled OOD test datasets, so that we could better leverage and deploy off-the-shelf trained models in risk-sensitive scenarios. Although progress has been made in this area, evaluation protocols in previous literature are inconsistent, and most works cover only a limited number of real-world OOD datasets and types of distribution shifts. To provide convenient and fair comparisons for various algorithms, we propose Out-of-Distribution Performance Prediction Benchmark (ODP-Bench), a comprehensive benchmark that includes most commonly used OOD datasets and existing practical performance prediction algorithms. We provide our trained models as a testbench for future researchers, thus guaranteeing the consistency of comparison and avoiding the burden of repeating the model training process. Furthermore, we also conduct in-depth experimental analyses to better understand their capability boundary. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_27263 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ODP-Bench: Benchmarking Out-of-Distribution Performance Prediction Yu, Han Li, Kehan Li, Dongbai He, Yue Zhang, Xingxuan Cui, Peng Machine Learning Recently, there has been gradually more attention paid to Out-of-Distribution (OOD) performance prediction, whose goal is to predict the performance of trained models on unlabeled OOD test datasets, so that we could better leverage and deploy off-the-shelf trained models in risk-sensitive scenarios. Although progress has been made in this area, evaluation protocols in previous literature are inconsistent, and most works cover only a limited number of real-world OOD datasets and types of distribution shifts. To provide convenient and fair comparisons for various algorithms, we propose Out-of-Distribution Performance Prediction Benchmark (ODP-Bench), a comprehensive benchmark that includes most commonly used OOD datasets and existing practical performance prediction algorithms. We provide our trained models as a testbench for future researchers, thus guaranteeing the consistency of comparison and avoiding the burden of repeating the model training process. Furthermore, we also conduct in-depth experimental analyses to better understand their capability boundary. |
| title | ODP-Bench: Benchmarking Out-of-Distribution Performance Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.27263 |