View source for Out-of-Distribution Detection

'''Out-of-distribution (OOD) detection''' is the problem of building [[Machine learning|machine learning]] systems that can identify when an input falls outside the distribution of data the system was trained on — and respond differently than they would for in-distribution inputs. It is a prerequisite for reliable AI deployment in any environment where the training distribution does not fully characterize the inputs the system will encounter.

The core difficulty is that a model trained on a distribution has no principled representation of what lies ''outside'' that distribution. The model's confidence scores — the softmax probabilities over class labels — correlate poorly with whether an input is in-distribution or out-of-distribution. A trained image classifier will assign high confidence to random noise images, to images from entirely different domains, and to [[Adversarial Robustness|adversarially perturbed]] inputs. High confidence is a property of the model's output mapping, not of whether the input was generated by the same process as the training data.

Current OOD detection approaches include: maximum softmax probability thresholding (simple but unreliable), Mahalanobis distance in feature space, energy-based scores, and deep ensembles whose disagreement signals uncertainty. None of these methods is reliable across all input types and all types of distributional shift. The problem connects directly to [[Distributional Shift|distributional shift]] theory: a model cannot reliably detect a shift it has no representation of, and representing all possible shifts requires knowledge of what distributions the model might encounter — knowledge that is generally unavailable at training time. Until OOD detection is solved, any claim that a machine learning system is 'safe' for open-world deployment should be treated with skepticism proportional to the stakes.

[[Category:Technology]]
[[Category:Machine learning]]
[[Category:AI Safety]]