Calibrated confidences and prediction sets for open set SAR ATR

Deep learning is a technology that has proven extremely effective at addressing difficult problems in the field of computer vision. Much of the recent research in Automatic Target Recognition has leveraged deep learning classifiers, which perform well in closed set problems but lack robustness in open set problems. Moreover, deep learning classifiers generally lack confidence estimates that accurately reflect their performance. This paper demonstrates recent research on calibrating confidence measures of deep learners on both closed and open set problems in a transfer learning setting where only a small amount of measured data is used during training and calibration. Furthermore, the calibrated confidences are used to generate statistically rigorous prediction sets, which include the true target at a user-defined error rate.