APPARATUS AND METHOD FOR CALIBRATING CONFIDENCE OF ARTIFICIAL NEURAL NETWORK

Abstract: an apparatus for calibrating confidence determines a correct logit element and a false logit element among a plurality of logit elements of a logit vector obtained by a neural network model performing a neural network operation on the training data, based on a correct class and a false class determined from a target probability distribution including target probabilities for each of a plurality of classes according to ground truth of the training data as elements, and determines whether to calibrate and a calibration value for a plurality of target probabilities of the target probability distribution based on a difference in value between the correct logit element and the false logit element.

Inventor(s): Bum Sub HAM, Hye Kang PARK, Jong Youn NOH, Young Min OH, Dong Hyeon BAEK

CPC Classification: G06N3/047 (Probabilistic or stochastic networks)

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