<![CDATA[Background: Machine learning models can give confident predictions when they receive inputs that are different from the data used during training. These predictions may not always be reliable when the model encounters unfamiliar inputs. OOD detection can help identify such inputs and reduce the risk of incorrect predictions in real-world applications.Aims: This study evaluates several OOD detection methods and examines their ability to reduce unreliable automatic predictions.Methods: Several post hoc detection methods were evaluated using ResNet classifiers trained on CIFAR datasets. The methods included Maximum Softmax Probability, ODIN, Energy-Based Detection, Mahalanobis Distance, Deep k Nearest Neighbors, and ReAct. The evaluation covered near-OOD, far-OOD, and corrupted-in-distribution conditions. Detection performance was also examined together with prediction errors, review needs, processing time, and memory use.Results: The methods showed different performance across the tested conditions. Deep k-Nearest Neighbors performed well for near OOD inputs, while ReAct showed strong performance for far OOD detection. The deployment simulation showed that ReAct-based gating reduced OOD leakage and improved the accuracy of predictions that were processed automatically. It also reduced incorrect predictions made without additional review. Energy-based detection provided competitive results with relatively low computational requirements.Conclusion: OOD detection can improve the reliability of machine learning systems. When combined with abstention and fallback mechanisms, unfamiliar inputs can be identified and sent for further review. This approach reduces unreliable predictions while allowing familiar inputs to continue through the automated process.]]>
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