shuolucs/Awesome-Out-Of-Distribution-Detection
[ACM CSUR 2025] Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advances
This is a curated collection of resources on out-of-distribution (OOD) detection, a critical task for ensuring the reliability of AI models. It organizes research papers by different approaches and application domains, providing a comprehensive overview of the field. Researchers and practitioners working with machine learning models will find this useful for understanding how to identify when their models encounter data unlike what they were trained on.
164 stars.
Use this if you are a machine learning researcher or practitioner looking for a structured overview of methods and applications for detecting unusual or unexpected data inputs in AI systems.
Not ideal if you are looking for an off-the-shelf software tool or code library to implement OOD detection immediately, as this is a list of research papers and not an executable project.
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Jan 02, 2026
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