  In the dynamic realms of machine learning and deep learning, the robustness
and reliability of models are paramount, especially in critical real-world
applications. A fundamental challenge in this sphere is managing
Out-of-Distribution (OOD) samples, significantly increasing the risks of model
misclassification and uncertainty. Our work addresses this challenge by
enhancing the detection and management of OOD samples in neural networks. We
introduce OOD-R (Out-of-Distribution-Rectified), a meticulously curated
collection of open-source datasets with enhanced noise reduction properties.
In-Distribution (ID) noise in existing OOD datasets can lead to inaccurate
evaluation of detection algorithms. Recognizing this, OOD-R incorporates noise
filtering technologies to refine the datasets, ensuring a more accurate and
reliable evaluation of OOD detection algorithms. This approach not only
improves the overall quality of data but also aids in better distinguishing
between OOD and ID samples, resulting in up to a 2.5\% improvement in model
accuracy and a minimum 3.2\% reduction in false positives. Furthermore, we
present ActFun, an innovative method that fine-tunes the model's response to
diverse inputs, thereby improving the stability of feature extraction and
minimizing specificity issues. ActFun addresses the common problem of model
overconfidence in OOD detection by strategically reducing the influence of
hidden units, which enhances the model's capability to estimate OOD uncertainty
more accurately. Implementing ActFun in the OOD-R dataset has led to
significant performance enhancements, including an 18.42\% increase in AUROC of
the GradNorm method and a 16.93\% decrease in FPR95 of the Energy method.
Overall, our research not only advances the methodologies in OOD detection but
also emphasizes the importance of dataset integrity for accurate algorithm
evaluation.
