ODP-Bench: Benchmarking Out-of-Distribution Performance Prediction

• URL: http://arxiv.org/abs/2510.27263v1
• Date: Fri, 31 Oct 2025 08:03:35 GMT
• Title: ODP-Bench: Benchmarking Out-of-Distribution Performance Prediction
• Authors: Han Yu, Kehan Li, Dongbai Li, Yue He, Xingxuan Zhang, Peng Cui,
• Abstract summary: Out-of-Distribution (OOD) performance prediction aims to predict the performance of trained models on unlabeled test datasets.<n>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.<n>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.
• Score: 29.953921358142477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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