Toward a Realistic Benchmark for Out-of-Distribution Detection

Abstract

Deep neural networks are increasingly used in a wide range of technologies and services, but remain highly susceptible to out-of-distribution (OOD) samples, that is, drawn from a different distribution than the original training set. A common approach to address this issue is to endow deep neural networks with the ability to detect OOD samples. Several benchmarks have been proposed to design and validate OOD detection techniques. However, many of them are based on far-OOD samples drawn from very different distributions, and thus lack the complexity needed to capture the nuances of real-world scenarios. In this work, we introduce a comprehensive benchmark for OOD detection, based on ImageNet and Places365, that assigns individual classes as in-distribution or out-of-distribution depending on the semantic similarity with the training set. Several techniques can be used to determine which classes should be considered in-distribution, yielding benchmarks with varying properties. Experimental results on different OOD detection techniques show how their measured efficacy depends on the selected benchmark and how confidence-based techniques may outperform classifier-based ones on near-OOD samples.

References in corpus (14)

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
• LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
• Object Detectors Emerge in Deep Scene CNNs
• Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
• Energy-based Out-of-distribution Detection
• Predictive Uncertainty Estimation via Prior Networks
• Scaling Out-of-Distribution Detection for Real-World Settings
• Exploring the Limits of Out-of-Distribution Detection
• Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?
• Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
• A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges
• Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series
• No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets