Search Results for author:
Found 2 papers, 1 papers with code
What is Wrong with One-Class Anomaly Detection?
From a safety perspective, a machine learning method embedded in real-world applications is required to distinguish irregular situations.
Decomposing Texture and Semantics for Out-of-distribution Detection
Out-of-distribution (OOD) detection has made significant progress in recent years because the distribution mismatch between the training and testing can severely deteriorate the reliability of a machine learning system. Nevertheless, the lack of precise interpretation of the in-distribution limits the application of OOD detection methods to real-world system pipielines.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
