# Deep Deterministic Uncertainity [[Uncertainty in Machine Learning]] can be levaraged as data-acquisition function for active learning. Aleatoric uncertainity (data uncertainity) - Ambiguous between multiple classes (i.e. data with high density but high entropy) - Doesn't decrease with more data. Epistemic uncertainity (model uncertainity) - Out-of-distribution (i.e. data point with low density) - Model unsure because it hasn't seen enough of similar data, decreases with more data. - Can be estimated by fitting a density function p(X) over data, in this case a [[Gaussian Mixture Model]] Goal is to acquire labels for the most efficient samples. - Label data with high epistemic uncertainity as that helps the model the most. - Labeling data with high aleatoric uncertainity isn't that helpful. ![[Deep Deterministic Uncertainity Algotirhm.png]] --- ## References