Label inference system

A label inference system comprising:

• a data generator configured to generate a training set and a test set, each including a plurality of images labeled with experts' annotations;
• a data trainer configured to perform training for a base model based on the generated training set and test set;
• a determiner configured to identify whether an evaluation metric f1 of the training model satisfies a base evaluation metric f1base;
• and
• a data inference unit configured to perform inference using the training set, the test set, and an unlabeled data set with the training model satisfying the base evaluation metric f1base,
• wherein the data inference unit calculates a current inference value of the unlabeled data set from a weighted average value of a previous inference value and a current estimated value according to the following Equation 1:
• {circumflex over (p)}t=Fμ({circumflex over (p)}t-1,Pt), 1
• wherein {circumflex over (p)}t represents the current inference value, {circumflex over (p)}t-1 represents a previous inference value, pt represents a current estimated value, Fμrepresents an online update operator with momentum μ, and t is a natural number.