Art Out of Distribution Detection

Art Out of Distribution (OOD) detection focuses on enabling machine learning models to reliably identify data points that differ significantly from their training data, preventing erroneous predictions on unseen inputs. Current research emphasizes improving robustness to imbalanced datasets and combining multiple detection scores for enhanced accuracy, exploring techniques like meta-analysis, spectral normalization, and tree-based ensembles, as well as leveraging pretrained vision-language models and autoencoders. This field is crucial for deploying trustworthy AI systems in real-world applications, particularly in safety-critical domains, by mitigating the risks associated with unexpected inputs and improving model reliability.