🤖 AI Summary
Existing out-of-distribution (OOD) detection methods are often confined to single technical paradigms or specific OOD categories, limiting their generalizability and robustness. To address this, we propose the Multi-Method Ensemble (MME) scoring framework—a unified, extensible OOD detection paradigm that systematically integrates feature truncation, multiple scoring functions (e.g., energy score, Mahalanobis distance), and logit-layer output fusion. Theoretical analysis and empirical evaluation demonstrate synergistic gains among components, substantially improving discrimination between near- and far-OOD samples. Evaluated on over ten benchmarks—including ImageNet-1K—using pre-trained models such as BiT, MME achieves state-of-the-art performance: an average false positive rate at 95% true positive rate (FPR95) of 27.57% on ImageNet-1K, outperforming the best prior method by 6 percentage points. This advancement establishes a more robust foundation for safety-critical open-world applications.
📝 Abstract
Detecting out-of-distribution (OOD) samples is essential for neural networks operating in open-world settings, particularly in safety-critical applications. Existing methods have improved OOD detection by leveraging two main techniques: feature truncation, which increases the separation between in-distribution (ID) and OOD samples, and scoring functions, which assign scores to distinguish between ID and OOD data. However, most approaches either focus on a single family of techniques or evaluate their effectiveness on a specific type of OOD dataset, overlooking the potential of combining multiple existing solutions. Motivated by this observation, we theoretically and empirically demonstrate that state-of-the-art feature truncation and scoring functions can be effectively combined. Moreover, we show that aggregating multiple scoring functions enhances robustness against various types of OOD samples. Based on these insights, we propose the Multi-Method Ensemble (MME) score, which unifies state-of-the-art OOD detectors into a single, more effective scoring function. Extensive experiments on both large-scale and small-scale benchmarks, covering near-OOD and far-OOD scenarios, show that MME significantly outperforms recent state-of-the-art methods across all benchmarks. Notably, using the BiT model, our method achieves an average FPR95 of 27.57% on the challenging ImageNet-1K benchmark, improving performance by 6% over the best existing baseline.