Abstract Subject matter regards improving machine learning techniques using informed pseudolabels. A ,thod can include receiving previously assigned labels indicating an expected classification for data, the )els having a specified uncertainty, generating respective pseudolabels for the data based on the aviously assigned labels, the data, a class vector determined by an ML model, and a noise model licating, based on the specified uncertainty, a likelihood of the previously assigned label given the iss, and substituting the pseudolabels for the previously assigned labels in a next epoch of training the - model. U) < C (< -i 0< F- U) LU < < r -1 0 UL) j U) LU U) < co -J < 0 o co < L0 L) LUuJ > .0) -
Machine learning using informed pseudolabels