Distribution Classifier

Distribution classifiers aim to reliably distinguish between in-distribution (ID) and out-of-distribution (OOD) data, a crucial aspect of robust machine learning. Current research focuses on improving the accuracy and calibration of OOD detection, particularly within semi-supervised learning settings and sparse models, often employing techniques like subspace-based anomaly detection, loss function modifications, and ensemble methods to enhance uncertainty estimation. These advancements are vital for improving the safety and reliability of deployed machine learning systems across various applications, mitigating risks associated with unexpected or adversarial inputs.